How to make progress in projecting climate change impacts
Bibliographic record
Abstract
Abstract Cheung, W. W. L., Pauly, D., and Sarmiento, J. L. 2013. How to make progress in projecting climate change impacts. – ICES Journal of Marine Science, 70: 1069–1074. Scientific modelling has become a crucial tool for assessing climate change impacts on marine resources. Brander et al. criticize the treatment of reliability and uncertainty of such models, with specific reference to Cheung et al. (2013, Nature Climate Change, 3: 254–258) and their projections of a decrease in maximum body size of marine fish under climate change. Here, we use the specific criticisms of Brander et al. (2013, ICES Journal of Marine Science) on Cheung et al. (2013) as examples to discuss ways to make progress in scientific modelling in marine science. We address the technical criticisms by Brander et al., then their more general comments on uncertainty. The growth of fish is controlled and limited by oxygen, as documented in a vast body of peer-reviewed literature that elaborates on a robust theory based on abundant data. The results from Cheung et al. were obtained using published, reproducible and peer-reviewed methods, and the results agree with the empirical data; the key assumptions and uncertainties of the analysis were stated. These findings can serve as a step towards improving our understanding of climate change impacts on marine ecosystems. We suggest that, as in other fields of science, it is important to develop incrementally (or radically) new approaches and analyses that extend, and ultimately improve, our understanding and projections of climate change effects on marine ecosystems.
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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 | 0.144 | 0.381 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".