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Use of Fragmented Landscapes by Marbled Murrelets for Nesting in Southern Oregon

2002· article· en· W1973686628 on OpenAlexfundno aff
Carolyn B. Meyer, Sherri L. Miller

Bibliographic record

VenueConservation Biology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersU.S. Geological SurveyCentre for Transportation Engineering and PlanningAndrew W. Mellon FoundationU.S. Environmental Protection Agency
KeywordsGeographyHabitatSeabirdBayFragmentation (computing)Old-growth forestVegetation (pathology)EcologyFisheryForestryPredationArchaeology

Abstract

fetched live from OpenAlex

Abstract: As old‐growth forest becomes more fragmented in the Pacific Northwest ( U.S.A.), species dependent on large patches of old‐growth forest may be at greater risk of extinction. The Marbled Murrelet ( Brachyramphus marmoratus ), a seabird whose populations are declining in North America, nests in such old‐growth forests or forests with large remnant trees. Using logistic regression models on landscapes in southern Oregon, we addressed (1) whether old‐growth forest fragmentation was associated with use of an area by murrelets and (2) whether proximity to certain marine features was associated with use of forest fragments by murrelets. On a geographic information system vegetation map derived from satellite imagery, we placed circular plots of 400‐, 800‐, 1600‐, and 3200‐m radius over surveyed inland areas occupied or unoccupied by murrelets. Within each plot, spatial and other land‐ and seascape habitat variables were calculated and regressed against murrelet occupancy. Murrelets generally occupied low‐elevation inland sites in landscapes with relatively low fragmentation and isolation of old‐growth forest patches, and these sites were close to the coast, river mouths, and a major bay. Almost all occupied landscapes occurred in a fog‐influenced vegetation zone. Because nesting habitat with large amounts of interior forest is currently scarce in southern Oregon, management efforts should focus on protecting or creating large, contiguous blocks of old‐growth forest, especially in areas near the coast.

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 score0.032
Threshold uncertainty score1.000

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)0.0010.000

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 head0.043
GPT teacher head0.243
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

Citations33
Published2002
Admission routes1
Has abstractyes

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