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Record W4401745233 · doi:10.1111/conl.13045

Ecosystem extent is a necessary but not sufficient indicator of the state of global forest biodiversity

2024· article· en· W4401745233 on OpenAlexaboutno aff
Simon Ferrier, Chris Ware, Jenet Austin, Hedley S. Grantham, Thomas D. Harwood, James Watson

Bibliographic record

VenueConservation Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersWildlife Conservation Society
KeywordsBiodiversityHeadlineEcosystemEnvironmental resource managementScope (computer science)Ecosystem servicesForest ecologyComponent (thermodynamics)EcologyEnvironmental scienceBusinessBiologyComputer science

Abstract

fetched live from OpenAlex

Abstract The Kunming‐Montreal Global Biodiversity Framework lays out an ambitious set of goals and targets aimed at halting and reversing biodiversity loss. The extent of natural ecosystems has been selected as one of a small set of headline indicators against which countries will report progress under this framework. We evaluate the effectiveness with which this indicator is expected to capture the overall scope of the ecosystem‐focused component of Goal A, and interlinkages with the species‐focused component of this same goal, using extensive global data on the integrity, connectivity, and plant species composition of forests. Results generated for all forest‐supporting countries demonstrate that consideration of these additional factors can profoundly alter understanding of the state of forest biodiversity relative to that based on extent alone. Employment of ecosystem extent as a headline indicator must therefore be augmented by appropriate use of component and complementary indicators addressing other key dimensions of ecosystem change.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.334

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 head0.011
GPT teacher head0.192
Teacher spread0.181 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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