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Record W2991220260 · doi:10.1139/facets-2019-0025

Are we accurately estimating the potential role of pollution in the decline of species at risk in Canada?

2019· article· en· W2991220260 on OpenAlexaffvenueabout
Jenny L. McCune, Sheila R. Colla, Laura E. Coristine, Christina M. Davy, D. T. Tyler Flockhart, Richard Schuster, Diane M. Orihel

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsQueen's UniversityUniversity of Northern British ColumbiaTrent UniversityMinistry of Natural Resources and ForestryCarleton UniversityOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaYork University
Fundersnot available
KeywordsPollutionScope (computer science)WildlifeEndangered speciesPopulationGeographyBiodiversityEnvironmental scienceEnvironmental protectionEnvironmental resource managementEnvironmental planningEcologyBiologyEnvironmental healthHabitatComputer science

Abstract

fetched live from OpenAlex

Pollution is a pervasive, albeit often invisible, threat to biodiversity in Canada. Currently, the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) relies on expert opinion to assess the scope (i.e., the proportion of a species’ population that may be affected) of pollution to species at risk. Here, we describe a spatially explicit, quantitative method for assessing the scope of pollution as a threat to species at risk in Canada. Using this method, we quantified the geographic co-occurrence of 488 terrestrial and freshwater species and pollution sources and determined that, on average, 57% of the mapped occurrences of each species at risk co-occurred with at least one pollution source. Furthermore, we found a weak correlation between the scope of the threat of pollution as assessed by COSEWIC expert panels and the geographic overlap of species occurrences and pollution sources that we determined with our quantitative method. Experts frequently identified scope of pollution as absent or negligible even for species with extensive co-occurrence with pollution sources, especially vascular plants. Clearly, a quantitative approach is needed to make accurate estimates of the scope of pollution as a threat to species at risk in Canada.

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 score0.387
Threshold uncertainty score0.625

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 head0.015
GPT teacher head0.218
Teacher spread0.204 · 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

Citations15
Published2019
Admission routes3
Has abstractyes

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