Corpus 1,272 assessed · 1,173 scored · 643 reproduced ≥75 · 168 flagged ·∅ 74.1/100
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Collisionless relaxation of a disequilibrated current sheet and implications for bifurcated structures.

Nat Commun · 2021
L1 95/100 3/4
Why this verdict

The main results reproduced: recomputed values matched the published ones within tolerance.

Reproduced on the brainbox compute brainarbeit.com
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q4 · Cause of the deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q2 · Endpoint comparability 🟡
Concordant (toward reproduced)
All content-critical questions reproduced
-4 pts
From: Q7 · Core claim 🟢
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score -3
✓ What held up
  • Same input data as the authors
  • Reported values are derivable from the shared data
  • Any deviation was negligible
  • The central claim held under reproduction
  • Overall, the reproduction was clean
What did not (or only partly)
  • 🟡Reported values were only indirectly comparable
  • 🟡A deviation arose in the data or preprocessing
  • 🟡A deviation was attributed to the published material
How its reproducibility compares
95/100
Reproducibility score
1.2 SD above mean
vs. all fields · 1173 studies
🎯 Scores higher than 89% of all assessed papers rank 105 of 1173 scored

A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.

Reproduction agent’s raw note

Described well enough to reproduce 1:1 from the SHIPPED data. The paper has three strands: an analytic orbit theory (not a pipeline -> out of scope), MMS spacecraft observations (pySPEDAS fitting; authors' scripts 'on request', not shipped -> not attempted), and a 1-D SMILEI PIC simulation whose outputs are publicly shipped on zenodo:4607112. We reproduced the PIC-derived results by downloading the 4.1 GB HDF5 to «infra» («our HPC» «job») and post-processing Fields0.h5 with standard h5py/numpy/scipy (P16: third-party tool on the paper's own data). The headline claim REPRODUCES cleanly: the initial single flat-top current layer relaxes into a BIFURCATED double current layer (2 Jz peaks separated by ~1.2 d_i, 33% central dip), developing by t10-30 wci^-1 and stable to t=100 («job», noise-robust). The layer thins ~3-4x (half-width 4.4 -> ~1.5 d_i, order-1 d_i as reported ~1 d_i) and intensifies ~5x; equilibration ~20-30 wci^-1 (reported ~30). Run setup (100 d_i / 32768 cells, t_max=100 wci^-1) matches exactly. NOT attempted: re-running the PIC sim (input deck not shipped, unnecessary), the MMS event analysis, and the analytic theory. C2/C3 graded within-tol because width/equilibration are definition-dependent; the physics is unambiguous. Grades are provisional; a human reviewer decides. No fabrication flags (all values derivable from shipped HDF5).

💻 Code ↗ 🗄 Data: 10.5281/zenodo.4607112

These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.

Assessment versions

Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.

  1. v1 current initial assessment Score 95
    assessed: 2026-06-15 ⛓ 85af911ddd5c
✎ I am an author of this paper

Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.

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Provenance — full disclosure

When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.

Reproduced
2026-06-15
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15
no human curator yet
Last updated
2026-08-05

Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.

Deep full-text extraction

Model: opus
Founding hypothesis

How does an initially disequilibrated collisionless current sheet relax or equilibrate, and what is the origin of commonly observed bifurcated current sheets?

Core claims
  • Collisionless transitions among four single-particle orbit classes are responsible for the relaxation/equilibration of a disequilibrated current sheet. mechanism
  • Particle orbits in a magnetic field reversal can be comprehensively categorized into four orbit classes based on the effective potential well shape and bounce-averaged z-velocity. finding
  • Bifurcated current sheets naturally arise from the equilibration process because two of the orbit classes necessarily exhibit spatially bifurcated structures. mechanism
  • The final equilibrium is most naturally described by the relative population of the four orbit-class phase-space distributions rather than a closed-form Maxwellian. finding
  • Particle-in-cell simulation equilibrium profiles agree well with MMS spacecraft observations of an electron-scale current sheet. finding
  • Single-particle orbit-class analysis combined with particle-in-cell simulations is used as the analytical framework. method
Experimental setups
Assay System Perturbation Readout Platform
Single-particle orbit/Hamiltonian analysis (effective potential of Harris current sheet) Harris current sheet model (collisionless plasma, ions and electrons) none Particle orbit classification via effective potential wells and bounce-averaged velocity
Particle-in-cell (PIC) simulation Disequilibrated Harris current sheet plasma initial disequilibrium (relaxation) Current sheet density, temperature, current strength, orbit-class populations during relaxation
Spacecraft in-situ measurements Earth's magnetotail electron-scale current sheet none Current sheet profiles (compared to PIC equilibrium) Magnetospheric Multiscale (MMS)
Key results
  • Effective potential exhibits a single-well shape for positive p_z and a double-well (with local hill at x=0) for negative p_z, defining non-crossing vs crossing (double-well) orbit classes.
  • Double-well orbit class splits into two subclasses with positive or negative bounce-averaged z-velocity depending on x-oscillation amplitude/energy.
  • Bifurcated current sheets emerge during relaxation as orbit-class transitions populate the spatially bifurcated orbit classes.
  • PIC equilibrium profiles match MMS electron-scale current sheet observations.
Key statistics
  • other ~25% of the time Cluster was in the magnetotail current sheet (bifurcated structures detected) (Frequency of bifurcated current sheet observation by Cluster)

Statistical methods review

Model: sonnet

A neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.

This plasma physics paper combines analytical single-particle orbit theory with particle-in-cell (PIC) simulations to study collisionless current sheet relaxation. The approach classifies particles into four orbit classes using constants of motion, derives their phase-space density contributions, and uses PIC simulations to track orbit-class transitions over time. Results are reported by comparing spatial profiles (density, temperature, current density) from PIC simulations with MMS spacecraft observations qualitatively. No inferential statistics or hypothesis tests are employed; the evidence is primarily mathematical derivation and profile agreement between simulation and observation.

Replicationunclear GroupsAnalytical orbit classes (NC, DW−, DW+, single-well) in PIC simulations; PIC simulation profiles compared to MMS spacecraft observations Pairingna Randomization/blindingna Dispersionnone
Approaches that could also have been used
  • Simulation profiles are compared to MMS spacecraft observations through visual, qualitative overlay of spatial profiles
    Could also: A quantitative goodness-of-fit metric (e.g., reduced chi-squared, RMSE, or Pearson correlation between observed and simulated profiles) could also be computed — A formal fit statistic would provide a reproducible, scalar summary of agreement, making it easier to assess sensitivity to simulation parameters and to compare across different candidate models
  • PIC simulation results are reported from what appear to be single simulation runs per configuration
    Could also: Running an ensemble of PIC simulations with varied random seeds or initial perturbation amplitudes and reporting the mean and spread across realizations would also be possible — Ensemble statistics would distinguish robust, reproducible outcomes from run-to-run variability inherent to stochastic particle initialization, which is especially relevant for transition timescales
  • Orbit-class populations are described in terms of phase-space volume arguments and qualitative trends
    Could also: Particle counts per orbit class could be tracked as time series with explicit uncertainty bounds derived from finite particle statistics (Poisson counting error ~√N) — Explicit counting uncertainties would clarify whether observed population shifts during relaxation are statistically distinguishable from sampling noise, given that PIC macro-particle counts are finite
  • The transition of particle orbits among classes over time is described qualitatively from PIC diagnostics
    Could also: A transition-rate matrix (analogous to a Markov chain) fitted to the simulation time series could also quantify the rates and directionality of orbit-class transitions — A quantitative rate model would allow direct comparison with any analytic estimates and would give a compact, reproducible summary of the relaxation dynamics
Software: Particle-in-cell (PIC) simulation code

Result convergence & founder nodes

Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.

Citation network

Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.

Citations
17
Impact: medium
Foundation confidence
None of its references are in our reproducibility record yet — its foundation cannot be assessed.
Topics

No assessed neighbours yet — the network grows as more papers are assessed.

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

Scope — pmid-34145266

Paper: Yoon, Yun, Wendel, Burch (2021), Collisionless relaxation of a disequilibrated current sheet and implications for bifurcated structures, Nat Commun 12:3774. DOI 10.1038/s41467-021-24006-x.

What the paper does

Two strands:

  1. Theory — analytic orbit theory (NC / DW± / M particle classes) explaining why a disequilibrated (under-heated) Harris current sheet relaxes collisionlessly into a bifurcated (double-peaked current) structure. → Out of scope (pencil-and-paper, not a pipeline).
  2. PIC simulation — a 1D Smilei particle-in-cell run of a disequilibrated Harris sheet, post-processed into Figs 2–4 (phase-space distributions, streak plots of B_y/J_z/T_i/n_i, and distribution-function evolution). → In scope: the simulation OUTPUTS are shipped on Zenodo, and the figure quantities are derived by post-processing those HDF5 files.
  3. MMS observations (Fig 1, Fig 5) — spacecraft data analyzed with pySPEDAS. → Partly out of scope for this run (event identification + manual fitting; the analysis code is "available from Y.D.Y. upon request" = not shipped).

Data / code availability (verbatim from paper)

  • Data: "The data from the PIC simulations are available from 10.5281/zenodo.4607112." (Zenodo record = CurrentSheetEquilibrationPIC.zip, 4.1 GB, HDF5: Fields0.h5 = EM fields + current/charge density; ParticleBinning0–6.h5 = ion dist (x,px,pz) + ion pressure-tensor components; ParticleBinning7–13.h5 = same for electrons.)
  • Code: SMILEI (open source, smileipic.github.io/Smilei); MMS analysis via pySPEDAS; "codes used in the data analyses are available from Y.D.Y. upon reasonable request" (i.e. the authors' own post-processing scripts are NOT shipped).

Reproduction strategy (third-party tool on the paper's own data — P16-valid)

We do not re-run the 6×10^8-particle PIC simulation (heavy, input deck not shipped, and not necessary). Instead we apply standard HDF5 post-processing (h5py/numpy/scipy) to the authors' shipped simulation outputs and regenerate the well-specified quantitative claims:

Claim Reported Source
C1 Bifurcation initial single-peaked current sheet relaxes to a double-peaked (bifurcated) current profile Figs 3–4, text
C2 Final half-width final current-sheet half-width ~0.1λ = 1 d_i = 10 d_e (from initial λ = 10 d_i) Results text, Fig 4
C3 Equilibration time system equilibrates at ~30 ω_ci⁻¹ Fig 3, text
C4 Run setup domain 100 d_i = 32768 cells, m_i/m_e=100, T=0.2 T_eq, t_max=100 ω_ci⁻¹, Δt=7.63×10⁻⁴ ω_ci⁻¹ Methods (cross-check vs HDF5 grid/time axis)

These are pipeline-derived (post-processing of shipped simulation output). Out of scope / not attempted: re-running the PIC simulation; the MMS event fitting (analysis code not shipped); the analytic orbit theory.

Compute plan

All on «our HPC» («infra»). One SLURM job (partition std, no --mem): build conda env on the compute node (has internet), download the Zenodo zip to «infra», extract, run analyze_pic.py, emit small JSON + PNG back to «host». No raw/intermediate data leaves «infra».

Figures / tables: Fig 3Fig 4
C1
Reported
disequilibrated single current sheet relaxes into a bifurcated (double-peaked Jz) current structure
Reproduced
Jz t=0 single flat-top -> by t>=10 wci^-1 two peaks; equilibrated (t=30-100) stable 2 peaks at x=49.4 & 50.6 d_i, separation ~1.19 d_i, central dip ~33%
exact
C2
Reported
final half-width ~0.1 lambda = 1 d_i (initial lambda=10 d_i)
Reproduced
half-width 4.4 -> ~1.4-1.6 d_i; each bifurcated channel ~1 d_i wide; peak Jz intensified ~5x
within tolerance
C3
Reported
equilibration at ~30 omega_ci^-1
Reproduced
half-width plateaus by t~20; bifurcation fully developed by t~30 wci^-1
within tolerance
C4a
Reported
domain 100 d_i, 2^15 = 32768 cells
Reproduced
Fields0.h5 = 32769 nodes = 32768 cells, x in [0,100] d_i
exact
C4b
Reported
t_max=100 wci^-1, dt=7.63e-4 wci^-1
Reproduced
iter_max*dt = 99.62 wci^-1; 103 field dumps
exact
C4c
Reported
shipped output = EM fields + current/charge density (Smilei)
Reproduced
Fields0.h5 has Jz/By/Rho; ParticleBinning0-13 = ion/electron dist + pressure tensors
exact

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 95/100

An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.

🟢1. Data identity
🟡2. Endpoint comparability
🟡3. Location of the main deviation
🟡4. Cause of the deviation
🟢5. Derivability / plausibility
🟢6. Severity of the deviation
🟢7. Core claim
🟢8. Severity of the miss (overall human judgment)
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q4 · Cause of the deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q2 · Endpoint comparability 🟡
Concordant (toward reproduced)
All content-critical questions reproduced
-4 pts
From: Q7 · Core claim 🟢
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score -3

Using the authors' shipped Smilei PIC outputs (zenodo:4607112) through an independent h5py/numpy/scipy pipeline, the headline bifurcation claim reproduces 1:1 — a single flat-top current sheet relaxes into two stable Jz peaks (x=49.4 & 50.6 d_i, sep ~1.2 d_i, 33% dip), and the run setup (32768 cells, t_max100 wci^-1, shipped fields) matches exactly. The only deviations are on the definition-dependent quantities: reproduced half-width ~1.4-1.6 vs reported ~1 d_i and equilibration ~20 vs 30 wci^-1 — these sit on our methodology (choice of width/threshold metric), not the authors' side, and the paper states both with ''. No fabrication concern: all compared values are derivable from the shipped HDF5. Overall a solid, essentially 1:1 reproduction.

🤝
Reproduced automatically — and fairly

Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.

Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.

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Reproduction footprint

claude-opus-4-8

Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.

149.3 k
tokens (I/O) · 12.1 M incl. cache
31 min
runtime · 0.06 CPU-h
0.2 GB
peak RAM
2
HPC jobs
hummel
machine