A framework for modelling fish and shellfish responses to future climate change
Bibliographic record
Abstract
Abstract Hollowed, A. B., Bond, N. A., Wilderbuer, T. K., Stockhausen, W. T., A'mar, Z. T., Beamish, R. J., Overland, J. E., and Schirripa, M. J. 2009. A framework for modelling fish and shellfish responses to future climate change. – ICES Journal of Marine Science, 66: 1584–1594. A framework is outlined for a unified approach to forecasting the implications of climate change on production of marine fish. The framework involves five steps: (i) identification of mechanisms underlying the reproductive success, growth, and distribution of major fish and shellfish populations, (ii) assessment of the feasibility of downscaling implications of climate scenarios derived from Intergovernmental Panel on Climate Change (IPCC) models for regional ecosystems to select and estimate relevant environmental variables, (iii) evaluation of climate model scenarios and select IPCC models that appear to provide valid representations of forcing for the region of study, (iv) extraction of environmental variables from climate scenarios and incorporation into projection models for fish and shellfish, and (v) evaluation of the mean, variance, and trend in fish and shellfish production under a changing ecosystem. This framework was applied to forecast summer sea surface temperature in the Bering Sea from 2001 to 2050. The mean summer surface temperature was predicted to increase by 2°C by 2050. The forecasting framework was also used to estimate the effects of climate change on production of northern rock sole (Lepidopsetta polyxystra) through projected changes in cross-shelf transport of larvae in the Bering Sea. Results suggest that climate change will lead to a modest increase in the production of strong year classes of northern rock sole.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".