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Record W3118017901 · doi:10.7717/peerj.9743

The Aichi Biodiversity Targets: achievements for marine conservation and priorities beyond 2020

2020· article· en· W3118017901 on OpenAlexafffund
Hannah Carr, Marina Abas, Loubna Boutahar, Olivia N. Caretti, Wing Yan Chan, Abbie S. A. Chapman, Sarah N. de Mendonça, Abigail Engleman, Filippo Ferrario, Kayelyn R. Simmons, Jana Verdura, Anna Zivian

Bibliographic record

VenuePeerJ · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversité LavalDalhousie University
FundersNatural Environment Research CouncilFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaInstitut Nordique De Recherche En Environnement Et En Santé Au Travail
KeywordsConvention on Biological DiversityBiodiversityMarine biodiversityEnvironmental planningWork (physics)Environmental resource managementMarine conservationResilience (materials science)BusinessPolitical scienceGeographyEcologyEngineeringEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

In 2010 the Conference of the Parties (COP) for the Convention on Biological Diversity revised and updated a Strategic Plan for Biodiversity 2011-2020, which included the Aichi Biodiversity Targets. Here a group of early career researchers mentored by senior scientists, convened as part of the 4th World Conference on Marine Biodiversity, reflects on the accomplishments and shortfalls under four of the Aichi Targets considered highly relevant to marine conservation: target 6 (sustainable fisheries), 11 (protection measures), 15 (ecosystem restoration and resilience) and 19 (knowledge, science and technology). We conclude that although progress has been made towards the targets, these have not been fully achieved for the marine environment by the 2020 deadline. The progress made, however, lays the foundations for further work beyond 2020 to work towards the 2050 Vision for Biodiversity. We identify key priorities that must be addressed to better enable marine biodiversity conservation efforts moving forward.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.414

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 science0.0000.002
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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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.

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

Citations31
Published2020
Admission routes2
Has abstractyes

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