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Record W2119205938 · doi:10.1139/z05-016

Wolf response to two kinds of barriers in an agricultural habitat in Spain

2005· article· en· W2119205938 on OpenAlexvenueno aff
Juan Carlos Blanco, Yolanda Cortés, Emílio Virgós

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersJunta de Castilla y León
KeywordsHabitatCanisPopulationWildlifeGray wolfGeographyEcologyPopulation declineBiologyDemography

Abstract

fetched live from OpenAlex

We examined the effect of two kinds of barriers on an expanding gray wolf, Canis lupus L., 1758, population in an agricultural habitat in north-central Spain. The barriers were (i) a four-lane fenced highway along a flat area without wildlife-crossing facilities, and (ii) the River Duero Artery (RDA), comprising the river itself (50–100 m wide) and several small infrastructures along it. From March 1997 to October 2001, all 4 radio-collared wolves living <15 km from the highway (1 adult territorial male, 1 territorial breeder female, 1 dispersing male, and 1 female in 3 periods of her life (territorial immature, disperser, and territorial breeder) crossed it on between 4% and 33% of 45–163 monitoring days via vehicle bridges. Moreover, 4 more highways that we monitored in areas without radio-collared wolves have not delayed expansion of the increasing wolf population, suggesting that these highways are not an important barrier for wolves in our study area. In contrast, only 3 of 8 wolves radio-collared <5 km from the RDA were detected crossing it, and 2 of those 3 started to cross it only after severe habitat disturbance; in addition, the RDA seems to have delayed wolf expansion for some 15 years, which suggests that it is a semipermeable barrier for wolves. We discuss the likely consequences of the RDA on the recovery of the Iberian wolf population.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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.

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

Citations84
Published2005
Admission routes1
Has abstractyes

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