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Record W2802855545 · doi:10.7939/r3p43k

Habitat selection and food-web relations of Horned Grebes (Podiceps auritus) and other aquatic birds on constructed wetlands in the Peace Parkland, Alberta, Canada

2009· article· en· W2802855545 on OpenAlexaboutno aff
Eva C. Kuczynski

Bibliographic record

VenueUniversity of Alberta Library · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatWetlandSelection (genetic algorithm)Food webGeographyFisheryEcologyBiologyPredation

Abstract

fetched live from OpenAlex

I investigated if constructed wetlands provide breeding habitat for the Horned Grebe (Podiceps auritus) in northwest Alberta. Over two years, I conducted bird surveys of 201 borrow-pits (ponds created during road construction) and 18 natural wetlands and collected data on local habitat and landscape features. For subsets of ponds, I also collected water chemistry and invertebrate data, and conducted stable isotope analysis. Grebes occurred on 36% of borrow-pits and produced chicks on 61% of occupied sites in 2007 and 81% in 2008. Grebes occurred more frequently on larger ponds, with more emergent vegetation, and avoided forested ponds that supported beaver activity. Horned Grebes are generalist foragers that did not select nesting ponds based on food-web structure. Twenty-six other bird species used borrow-pits, with distinct assemblages occurring on agricultural versus forested ponds. My study indicates that wetland construction offers a viable method for creating habitat for Horned Grebes and other 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)0.000
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 score0.397
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.147
Teacher spread0.144 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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