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Record W3092451893 · doi:10.1016/j.pecon.2020.08.001

Level-2 ecological integrity: Assessing ecosystems in a changing world

2020· article· en· W3092451893 on OpenAlexafffund
Charles A. Martin, Raphaël Proulx

Bibliographic record

VenuePerspectives in Ecology and Conservation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNaturalnessEnvironmental resource managementEcosystemContext (archaeology)Environmental scienceHabitatEcological indicatorEcologyBiological integrityScale (ratio)Biomass (ecology)Ecosystem servicesGeographyBiologyCartography

Abstract

fetched live from OpenAlex

Ecological integrity is a term often used to describe the state of ecosystems subjected to anthropogenic pressures. It is usually defined closely to the literal definition of integrity: being whole or unimpaired. Considering the deep changes our world is undergoing, we argue here for ecological indicators that are not restricted to naturalness targets. We propose a conceptual framework for so-called level-2 indicators of ecological integrity, that evaluate how the integrity of ecosystems is preserved given their naturalness context. We develop reference relationships between indicator and contextual variables and then assess how an ecosystem is doing, compared to others in similar contexts, by its distance to this reference. We explore two such relationships: the amount of aboveground phytomass an ecosystem stores in a given volume (biomass packing efficiency) and the mean patch size given the total habitat amount in the landscape (habitat connectivity). Using datasets at the national and worldwide scale, we show that these indicators are objective measures of ecological integrity that allow the comparison of plant stands and landscapes across different environmental and naturalness contexts. This framework provides a basis to evaluate if the state of an ecosystem is degrading and paves the way to a triage system prioritizing conservation and restoration actions.

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 score0.045
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.054
GPT teacher head0.291
Teacher spread0.236 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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