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Unequivocal principles for area-based biodiversity conservation

2023· preprint· en· W4386157039 on OpenAlexaff
Federico Riva, Nick M. Haddad, Lenore Fahrig

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiodiversityBiodiversity conservationPopulationEnvironmental resource managementHabitatGeographyEcosystemEcosystem servicesConservation scienceEnvironmental planningPolitical scienceEnvironmental ethicsEcologyEconomicsSociologyBiology

Abstract

fetched live from OpenAlex

Recent agreements have strengthened and expanded ongoing international commitments to protect and restore native habitats. Nevertheless, how such commitments should be implemented has been historically controversial, and nuances in ongoing debates are often misunderstood, hindering biodiversity conservation. We propose three unequivocal principles that must be central to how area-based biodiversity conservation will occur in the coming decades. These principles relate to habitat coverage, amount, and connectivity, and their enunciation clarifies apparent contradictions in the literature. We explain why socio-economic considerations that are central to current biodiversity conservation cannot override these principles. Biodiversity must be supported everywhere on Earth, especially when considering the right of human population to access nature and to benefit from countless 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.041
Scholarly communication0.0130.014
Open science0.0040.011
Research integrity0.0080.017
Insufficient payload (model declined to judge)0.0060.003

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 head0.127
GPT teacher head0.249
Teacher spread0.122 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2023
Admission routes1
Has abstractyes

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