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Record W7096951800

Birds, Broom, Bunnies, and Biplanes: Conserving a Remnant Population of Coastal Vesper Sparrows at the Nanaimo Airport,

2015· article· en· W7096951800 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSparrowDisjunctSubspeciesHabitatPopulationEndangered species
DOInot available

Abstract

fetched live from OpenAlex

Extended Abstract: The coastal vesper sparrow (Pooecetes gramineus affinis) forms a disjunct population of the vesper sparrow (Pooecetes gramineus), and breeds from southwestern Vancouver Island, British Columbia (B.C.) south through western Washington and Oregon to the extreme northwest of California (Beauchesne 2003). This subspecies was probably never common in British Columbia, and it is assumed that prior to European settlement, sparsely vegetated Garry oak meadowland or burnt areas would have been the key habitats used by this subspecies. During the latter part of the 20th century, significant areas of open land, farmlands, and Garry oak meadowlands have been converted to industrial, commercial, residential, and intensively farmed land; therefore, it is probable that as suitable habitat declined, so did numbers of the coastal vesper sparrow. Consideration is being given to listing the coastal vesper sparrow as in danger of extirpation in Washington and Oregon. The subspecies is red-listed (Threatened) in British Columbia. The Garry Oak Ecosystems Recovery Team (GOERT) established a list of priority species for future research and recovery in the Georgia Basin region of British Columbia. These species are either in decline or are currently extirpated from the region, and they rely on Garry oak

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)0.000
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 score0.966
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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