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Record W2513525377 · doi:10.1894/0038-4909-61.3.203

Rock wren transport in railroad boxcars

2016· article· en· W2513525377 on OpenAlexaboutno aff
Nathanial Warning

Bibliographic record

VenueThe Southwestern Naturalist · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsVagrancyBiological dispersalGeographyEcologyOccupancyHabitatRange (aeronautics)BiologyDemographyPopulation

Abstract

fetched live from OpenAlex

In 1956, and again in 1988, breeding pairs of rock wrens (Salpinctes obsoletus) were observed in Churchill, Manitoba, beyond their previously known breeding extent. It has been suggested that these and other vagrant rock wrens might have been accidentally transported in railroad boxcars. Alternative explanations include: 1) that rock wrens are prone to vagrancy during migration and dispersal, and 2) that the species is expanding its range via hydrographic or human-made corridors. I compiled northern and eastern vagrancy records from May 1898 through November 2015 for rock wrens and four other bird species with similar ranges. I calculated the distance of each sighting to the nearest railway and hydrographic features, compared these distances among species, and identified vagrant clusters. Rock wren vagrants were not significantly different from vagrants of lazuli buntings (Passerina amoena) or Bullock's orioles (Icterus bullockii) in their proximity to railways. Results suggest that vagrancy rates of rock wrens are similar to those of sage thrashers (Oreoscoptes montanus) and green-tailed towhees (Pipilo chlorurus), and vagrancy is likely driven by landscape factors rather than the use of railway corridors or accidental transport in freight cars. Proximity of vagrant rock wrens to lakes, reservoirs, rivers, and streams indicates that these and associated habitat features may act as travel and dispersal corridors.

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 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 score0.073
Threshold uncertainty score0.999

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)0.0010.002

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 head0.208
Teacher spread0.200 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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