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Record W2008688100 · doi:10.1676/07-109.1

Does age influence territory size, habitat selection, and reproductive success of male Canada Warblers in central New Hampshire?

2008· article· en· W2008688100 on OpenAlexaboutno aff
Leonard R. Reitsma, Michael T. Hallworth, Phred M. Benham

Bibliographic record

VenueThe Wilson Journal of Ornithology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsFledgeWarblerHabitatEcologyGeographyReproductive successSwampDeciduousBiologyDemographyHatchingPopulation

Abstract

fetched live from OpenAlex

The Canada Warbler (Wilsonia canadensis) is currently in decline in the northeastern United States and basic demographic parameters remain to be described. We studied marked populations (76 ASYs, 14 SYs, and 2 of unknown age) of Canada Warblers on two study sites from 2003 to 2006. We mapped 92 territories (including males returning in multiple years) of 71 males using handheld GPS and ArcMap. We compared the pairing and fledging success of older and younger males on both sites, a red maple (Acer rubrum) swamp and a young forest intensively harvested in 1985 with ∼10% residual standing trees used by males as song perch trees. Both sites had a high proportion of ASYs (84% ASY for all territorial males, 77.5% of all males including non-territorial individuals). Both pairing (91%) and fledging (78%) success was comparatively high suggesting these two sites were of high value to this species. A higher proportion of SYs were transients. Pairing success was lower for younger males which established territories, but paired SYs fledged at least one young at a rate comparable to older males. This study corroborates the benefits of age and experience to reproductive performance. The results suggest that both red maple swamps and post-harvest forests with thick subcanopy vegetation and emergent trees provide high quality habitat for breeding Canada Warblers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.964

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)0.0000.000

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 head0.007
GPT teacher head0.198
Teacher spread0.192 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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