Data for: Temperature variation and life history drive nonlinear dynamics of marine fish populations
Bibliographic record
Abstract
Description: This repository contains the data needed to reproduce our analyses. All code has been made available to reviewers and will be publicly released upon manuscript acceptance. The raw data come from: Fish time series from the RAM Legacy Stock Assessment Data Base (Ricard et al. 2011; Fish and Fisheries) Fish species life history traits from FishLife (Thorson et al. 2023; Methods in Ecology Evolution) Sea surface temperature time series from NOAA OI SST V2 High Resolution Dataset (https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html) Sea surface temperature time series from COBE-SST 2 Dataset (https://psl.noaa.gov/data/gridded/data.cobe2.html) Data files: timeseries_ssb.csv - time series of spawner biomass timeseries_rec.csv - time series of recruitment timeseries_tc.csv - time series of total catch mean_annual_SST_noaa.oisst.v2.csv - time series of mean SST for each population from noaa.oisst.v2 variation_annual_SST_noaa.oisst.v2.csv - time series of within year SST variation for each population from noaa.oisst.v2 data_correlates_sst_traits_ssb_r_tc.csv - dataframe of the life history and temperature correlates for all populations df_ccm_recruits.csv - time series of recruitment and matching SST for convergent cross mapping df_ccm_spawners.csv - time series of spawner biomass and matching SST for convergent cross mapping df_ccm_totalCatch.csv - time series of total catch and matching SST for convergent cross mapping SST_annualMean_timeseries_donors_COBE_ForSensitivityAnalysis.csv - time series of mean SST from COBE SST_annual_intraVariation_timeseries_donors_COBE_ForSensitivityAnalysis.csv - time series of within year SST variation from COBE datassb_forModels.csv - data for spawner mixed models datarec_forModels.csv - data for recruit mixed models
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.316 | 0.183 |
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".