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Priorities for Improving the Scientific Foundation of Conservation Policy in North America

2009· article· en· W2072958754 on OpenAlexaffabout
Reed F. Noss, Erica Fleishman, Dominick A. DellaSala, John Fitzgerald, Mart R. Gross, Martin B. Main, FIONA NAGLE, Stacey Lee O'Malley, Jon Rosales

Bibliographic record

VenueConservation Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Ecology, Wildlife Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlueprintPolitical scienceBiodiversityScience policyNatural resourceEnvironmental planningConservation biologyPublic policyEnvironmental resource managementEnvironmental ethicsPublic administrationGeographyEcologyEngineeringEconomicsBiology

Abstract

fetched live from OpenAlex

The Society for Conservation Biology (SCB) can enhance conservation of biodiversity in North America by increasing its engagement in public policy. Toward this end, the North America Section of SCB is establishing partnerships with other professional organizations in order to speak more powerfully to decision makers and taking other actions--such as increasing interaction with chapters--geared to engage members more substantively in science-policy issues. Additionally, the section is developing a North American Biodiversity Blueprint, which spans the continental United States and Canada and is informed by natural and social science. This blueprint is intended to clarify the policy challenges for protecting continental biodiversity, to foster bilateral collaboration to resolve common problems, and to suggest rational alternative policies and practices that are more likely than current practices to sustain North America's natural heritage. Conservation scientists and practitioners can play a key role by drawing policy makers' attention to ultimate, as well as proximate, causes of biodiversity decline and to the ecological and economic consequences of not addressing these threats.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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 designObservational
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

Citations11
Published2009
Admission routes2
Has abstractyes

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