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UNIQUE ISLAND HABITATS MAY BE THREATENED BY DOUBLE-CRESTED CORMORANTS

2005· article· en· W2199240496 on OpenAlexaffabout
Craig E. Hebert, Jason Duffe, D. V. Chip Weseloh, E. M. Senese, G. Douglas Haffner

Bibliographic record

VenueJournal of Wildlife Management · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of WindsorMinistry of Natural Resources and ForestryCarleton University
Fundersnot available
KeywordsCormorantGeographyNest (protein structural motif)Threatened speciesHabitatPopulationEcologyVegetation (pathology)Barrier islandPredationFisheryBiology

Abstract

fetched live from OpenAlex

Double-crested cormorant (Phalacrocorax auritus) populations on the Great Lakes expanded greatly during the past 2 decades. On Lake Erie, the number of breeding cormorants increased from 174 birds (87 nests) in 1979 to 26,542 (13,271 nests) in 2000. In 2000, 81% of the breeding population was on 2 western-basin islands (East Sister and Middle Islands). The plant communities on these islands represent some of the last remnants of Carolinian vegetation in Canada. Our study is the first to quantitatively assess the relationship between the distribution of nesting cormorants and forest health. On East Sister Island, 2 measures of forest cover were obtained using infrared aerial photographs and ground-based measurements of leaf area index. These measures of forest cover were correlated (rs = 0.70, P < 0.001), which validated the use of remotely sensed data to assess forest cover. Cormorant nest density was negatively correlated with tree cover on both East Sister and Middle Islands. Temporal comparisons of Middle Island data indicated a reduction in tree cover from 1995 to 2001, and these reductions coincided with a large increase in the island's cormorant population. Although correlational in nature, our results suggest that cormorants may be detrimentally affecting island forests.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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 designNot applicable
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

Citations67
Published2005
Admission routes2
Has abstractyes

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