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Record W2088701874 · doi:10.1139/x02-109

Habitat associations of black-backed and three-toed woodpeckers in the boreal forest of Alberta

2002· article· en· W2088701874 on OpenAlexvenueaboutno aff
Jeff S. Hoyt, Susan J. Hannon

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSalvage loggingWoodpeckerTaigaBlack spruceEcologyOccupancyBiological dispersalHabitatGeographyBorealForestryOld-growth forestBiologyPopulationSnagDemography

Abstract

fetched live from OpenAlex

Recent studies suggest that black-backed (Picoides arcticus) and three-toed woodpeckers (Picoides tridactylus) might decrease in abundance because of habitat loss from fire suppression and short-rotation logging in landscapes managed for forestry. We examined black-backed and three-toed woodpecker occupancy of stands in a 2-year post-fire forest, mature and old-growth spruce and pine forests, and six post-fire coniferous forests of different ages. Three-toeds were detected in old stands and in the 2-year-old burn, and their probability of occupancy of burned forests decreased between 3 and 8 years post-fire. Within 50 km of the 2-year-old burn, black-backs were only detected in the burn and not in old-growth or mature conifer stands. However, they did occupy old coniferous stands located 75 and 150 km from the recent burn. They had a similar probability of occupying stands in the 3-, 4-, and 8-year-old burns but were not detected in the 16-year-old burn. The persistence of three-toed woodpeckers in boreal Alberta will likely depend on the presence of both old-growth and recently burned coniferous forests or forests with old-growth structural characteristics. Black-backed woodpeckers appear to be more burn dependent than three-toeds, and their long-term persistence may depend on the frequency of recently burned forests within their dispersal range.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.717
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.273
Teacher spread0.237 · 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.

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

Citations112
Published2002
Admission routes2
Has abstractyes

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