Fisheries, food security, climate change, and biodiversity: characteristics of the sector and perspectives on emerging issues
Bibliographic record
Abstract
Abstract Rice, J. C., and Garcia, S. M. 2011. Fisheries, food security, climate change, and biodiversity: characteristics of the sector and perspectives on emerging issues. – ICES Journal of Marine Science, 68: 1343–1353. This paper reviews global projections to 2050 for human population growth and food production, both assuming constant climate and taking account of climate-related changes in growing conditions. It also reviews statistics on nutritional protein requirements, as well as how those requirements are met by fish on a regional basis. To meet projected food requirements, the production of fish has to increase by ∼50% from current levels. The paper also summarizes the main pressures on marine biodiversity that are expected to result from the impacts of changing climate on marine ecosystems, as well as the management measures and policy actions promoted to address those pressures. It highlights that most of the actions being proposed to address pressures on marine biodiversity are totally incompatible with the actions considered necessary to meet future food security needs, particularly in less developed parts of the world. The paper does not propose a solution to these conflicting pulls on policies for conservation and sustainable use. Rather, it emphasizes that there is a need for the two communities of experts and policy-makers to collaborate in finding a single compatible suite of policies and management measures, to allow coherent action on these crucial and difficult problems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".