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Record W2154115698 · doi:10.1093/icesjms/fsr041

Fisheries, food security, climate change, and biodiversity: characteristics of the sector and perspectives on emerging issues

2011· article· en· W2154115698 on OpenAlexaff
Jake Rice, Serge M. Garcia

Bibliographic record

VenueICES Journal of Marine Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsFood securityClimate changeBiodiversityNatural resource economicsBusinessEnvironmental resource managementPopulationFisheryEnvironmental planningGeographyEnvironmental scienceEcologyEconomicsAgriculture

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.040
GPT teacher head0.218
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations197
Published2011
Admission routes1
Has abstractyes

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