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Extinction risk and bottlenecks in the conservation of charismatic marine species

2011· article· en· W2097634738 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

VenueConservation Letters · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsThreatened speciesConservation-dependent speciesExtinction (optical mineralogy)BiodiversityConservation statusInvertebrateBiodiversity conservationMarine biodiversityEcologyGeographyMarine protected areaConservation biologyFisheryNear-threatened speciesBiologyHabitat

Abstract

fetched live from OpenAlex

Abstract The oceans face a biodiversity crisis, but the degree and scale of extinction risk remains poorly characterized. Charismatic species are most likely to garner greatest support for conservation and thus provide a best‐case scenario of the status of marine biodiversity. We summarize extinction risk and diagnose impediments to successful conservation for 1,568 species in 16 families of marine animals in the movie Finding Nemo . Sixteen percent (12–34%) of those that have been evaluated are threatened, ranging from 9% (7–28%) of bony fishes to 100% (83–100%) of marine turtles. A lack of scientific knowledge impedes analysis of threat status for invertebrates, which have 1,000 times fewer conservation papers than do turtles. Legal protection is severely deficient for sharks and rays; only 8% of threatened species in our analysis are protected. Extinction risk among wide‐ranging taxa is higher than most terrestrial groups, suggesting a different conservation focus is required in the sea.

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 score0.006
Threshold uncertainty score0.791

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)0.0000.000

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 head0.025
GPT teacher head0.185
Teacher spread0.160 · 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