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Record W4380875869 · doi:10.1002/9781119511847.ch19

Fisheries and Biodiversity Conservation

2023· other· en· W4380875869 on OpenAlexaff
Anthony Charles

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsConvention on Biological DiversityBiodiversityMeasurement of biodiversityIncentiveEnvironmental resource managementConservation PlanEndangered speciesEcosystem servicesAquatic biodiversity researchGeographyFisheries managementResource (disambiguation)FisheryBusinessEnvironmental planningEcosystemBiodiversity conservationEcologyFishingEnvironmental scienceBiologyHabitatEconomics

Abstract

fetched live from OpenAlex

This chapter explores how that imperative for ‘biodiversity conservation’ relates to fisheries. It reviews the types of interaction between fisheries and endangered aquatic species. The chapter looks at the state of decision-making in the fishery and in the biodiversity conservation field. Despite continuing tensions, and some setbacks, there are indications of expanding common ground between the biodiversity conservation and fisheries management streams. The opportunities for linking the two streams of fisheries and biodiversity conservation, for each scale or level of decision-making, from global to local, and progress towards common ground, is apparent across multiple governance levels. Some key incentives and opportunities help to link fisheries and biodiversity conservation. The chapter concludes with a focus on two major international institutions, since these have been instrumental in creating international frameworks for linking natural resource use and biodiversity conservation. These are the Convention on Biological Diversity and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.054
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0600.006

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 head0.011
GPT teacher head0.196
Teacher spread0.186 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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