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Record W2884860875 · doi:10.1002/jwmg.21538

Fisher predation on Canada lynx in the Northeastern United States

2018· article· en· W2884860875 on OpenAlexaboutno aff
Scott McLellan, Jennifer H. Vashon, Erica L. Johnson, Shannon M. Crowley, Adam D. Vashon

Bibliographic record

VenueJournal of Wildlife Management · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersNational Fish and Wildlife FoundationDavis Conservation FoundationInternational PaperU.S. Fish and Wildlife ServiceWildlife Conservation Society
KeywordsPredationThreatened speciesWildlifeGeographyPopulationHabitatRange (aeronautics)EcologyWildlife managementFisheryBiologyDemography

Abstract

fetched live from OpenAlex

ABSTRACT The geographic range of Canada lynx (Lynx canadensis) extends south from Canada into the United States where they are federally protected as a threatened species. Although inadequate protection of habitat on federal lands was the primary reason for listing, the status of lynx in the lower 48 states is not well understood. Thus, we initiated a telemetry study to assess the status of a lynx population in northern Maine, USA. In this manuscript, we present findings on a source of mortality not previously documented. Between 1999 and 2011, we captured 187 lynx, equipped 85 with radio‐collars, and investigated mortalities when they occurred. Predation was the leading source of mortality and accounted for ≥18 of 65 mortalities, 14 of which were attributed to fishers (Martes pennanti). Although fisher predation did not appear to restrict population growth during this study, we recommend that lynx and fishers be monitored where the species coexist to better inform management decisions. © 2018 The Wildlife Society.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.852
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 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
Published2018
Admission routes1
Has abstractyes

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