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Record W2103328307 · doi:10.1139/z06-134

Songbird response to seismic lines in the western boreal forest: a manipulative experiment

2006· article· en· W2103328307 on OpenAlexaffvenue
Craig S. Machtans

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsGovernment of Northwest Territories
Fundersnot available
KeywordsSongbirdBorealAbundance (ecology)ShrubTaigaEcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Millions of kilometres of seismic lines have been created for hydrocarbon exploration in the boreal forest and their impact on songbirds is unknown. I conducted a replicated before–after control–impact (BACI) field experiment in southern Northwest Territories to evaluate the impact of 6 m wide seismic lines on songbirds. Territories of all birds on six pairs of 12 ha control and treatment plots were mapped for one year before and one year after seismic lines were cut through the treatment plots. The songbird community was not dramatically affected by seismic lines. At the community level, birds did not decline in abundance or move their territories relative to the seismic lines, and they included the seismic lines in their territories. However, ground and shrub nesting species that had territories spanning the seismic lines increased the size of their territories. At the species level, only the Ovenbird ( Seiurus aurocapilla (L., 1766)) showed a consistent response to seismic lines. Ovenbirds declined in abundance, moved their territories away from seismic lines, and were not observed crossing the lines. Pressure on industry from land managers to reduce the width of seismic lines should continue to minimize the impact of these clearings on all species.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.237
Teacher spread0.190 · 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 designBench or experimental
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

Citations55
Published2006
Admission routes2
Has abstractyes

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