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Gaining Traction: Retreading the Wheels of Marine Conservation

2009· article· en· W2008908156 on OpenAlexaff
Phaedra Doukakis, E. C. M. Parsons, William C. G. Burns, Anne K. Salomon, Ellen Hines, John A. Cigliano

Bibliographic record

VenueConservation Biology · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTraction (geology)GeographyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A number of international treaties address the conservation of marine resources. The declining state of the world's oceans suggests that these treaties are not succeeding and could use improvement. The Convention on International Trade in Endangered Species (CITES) is increasingly embracing the conservation of marine species. We examine the evolution of marine species protection under CITES and illuminate some of the mechanisms used and challenges faced in implementing CITES protection. We found that clarification is needed on when and where CITES applies and how CITES should work with other treaties and institutions. The Society for Conservation Biology (SCB) can contribute to increased effectiveness of CITES for marine conservation. Foremost, the SCB community could foster dialogue on creating a broad vision of how CITES should apply to marine species and how it can synergistically interact with other important marine-conservation treaties and institutions. More specific contributions could focus on defining listing criteria for marine species, improving the science behind the nondetriment finding, and offering technical guidance on species proposals. A future role for SCB could be to contribute to the enhanced effectiveness of other marine conservation agreements such as the Convention on the Conservation of Migratory Species of Wild Animals, the International Whaling Commission, and the United Nations Convention on the Law of 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.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.999

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)0.0020.000

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 head0.045
GPT teacher head0.254
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations15
Published2009
Admission routes1
Has abstractyes

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