Global nutrient cycling by commercially targeted marine fish (Le Mézo et al., 2022, Biogeosciences)
Bibliographic record
Abstract
Model outputs and code used for the study "Global nutrient cycling by commercially targeted marine fish" published in Biogeosciences (Le Mézo et al., 2022). composite4b_cycling_MCV3_PotH_LME_2010_R2_2010_fNPP7_ks24_p50_c7_newFNPP_Online_nruns_31 = model outputs with composite.maps contains the 2D fields y200 refers to fields at the pristine state and yglo to fields at the global peak catch. dfish is the fish biomass in wet weight per gram (size class) resp5 is the cycling rate that was used in the paper (defined in the Methods section) Main_script.mlx = main code used to compute the nutrient content and cycling of fish and the comparisons with other fields size_bins_width.m = code used to compute the model size bin width data_annual.mat = NO3, PO4, NPP, C export fields composite_MCV3_PotH_LME_2010_R2_2010_fNPP7_ks24_p50_c7_newFNPP_Online_nruns_31.mat is the model outputs with cyc.composite.maps.yglo.mean.harvest being the catch field at global peak catch solublefraction_Mahowald2009_360x180.nc is the Fe deposition field Brahneyetal2015_nitrogenandphosphorus2x2annualdep_360x180.nc is the N deposition field mask_LME.mat is the mask of LME areas ocean_topaz_tracers.timmean.BOATS_grid_fed.nc is the modeled dissolved Fe concentrations in seawater by the TOPAZ model zeu_lee_modis_aqua_average_2002-2019.nc is the euphotic depth field used to compute the nutrient concentrations woa05_nitrate_month.nc is the NO3 field used to make the spatial interpolations of the Fe:C stoichiometric ratios. mass_50sizes_boats.mat is the mass of each size class of the BOATS model Tables S2 and S3.xlsx litterature compilation of values for N and P in fish and zooplankton Galbraith et al (2019) SI.pdf is the supplement to Galbraith et al. (2019) in which the data compilation for the Fe content of fish, zooplankton and phytoplankton can be found.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.032 |
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".