Data and code to next-generation ensemble projections reveal higher climate risks for marine ecosystems
Bibliographic record
Abstract
Data products: Tittensor et al. (2021). Next-generation ensemble projections reveal higher climate risks for marine ecosystems, Nature Climate Change. DOI: https://doi.org/10.1038/s41558-021-01173-9 This data was produced using R scripts available on the GitHub repository https://github.com/Fish-MIP/CMIP5vsCMIP6, and was used for analysis and plotting in Tittensor et al. (2021). These R scripts are also available here as CMIP5vsCMIP6_code.zip Data_CMIP5.Rdata and Data_CMIP6.RData include all data used to produce global maps of percentage change in total consumer biomass. Data_trends_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in total consumer biomass. Data_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce global maps of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature. Data_trends_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature. The suffix _reducedModelSet refers to the case when only the subset of Fish-MIP models in Lotze et al. (2019) - Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change, PNAS, DOI: https://doi.org/10.1073/pnas.1900194116 - are considered. This data was used to produce some of the supplementary figures in Tittensor et al. (2021). Please contact Derek Tittensor (derek.tittensor@dal.ca), Camilla Novaglio (camilla.novaglio@gmail.com), or Julia Blanchard (julia.blanchard@utas.edu.au) for data interpretation and use.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.181 | 0.124 |
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".