Comment on gmd-2020-423
Bibliographic record
Abstract
A simplified model, representing the dynamics of marine organic particles in a given size range experiencing coagulation and fragmentation reactions, is developed. The framework is based on a discrete size spectrum on which reactions act to exchange properties between different particle sizes. The reactions are prescribed according to triplet interactions. Coagulation combines two particle sizes to yield a third one, while fragmentation breaks a given particle size into two (i.e. the inverse of the coagulation reaction). The complete set of reactions is given by all the permutations of two particle sizes associated with a third one. Since, by design, some reactions yield particle sizes that are outside the resolved size range of the spectrum, a closure is developed to take into account this unresolved range and satisfy global constraints such as mass conservation. In order to minimize the number of tracers required to apply this model to an ocean general circulation model, focus is placed on the robustness of the model to the particle size resolution. Thus, numerical experiments were designed to study the dependence of the results on (i)Â the number of particle size bins used to discretize a given size range (i.e. the resolution) and (ii)Â the type of discretization (i.e. linear vs. nonlinear). The results demonstrate that in a linearly size-discretized configuration, the model is independent of the resolution. However, important biases are observed in a nonlinear discretization. A first attempt to mitigate the effect of nonlinearity of the size spectrum is then presented and shows significant improvement in reducing the observed biases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.012 |
| Insufficient payload (model declined to judge) | 0.289 | 0.236 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".