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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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