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Record W2152880971 · doi:10.1525/cond.2010.090078

Variation in the Nesting Habits of Clapper Rails in Tidal Marshes of the Northern Gulf of Mexico

2010· article· en· W2152880971 on OpenAlexaff
Scott A. Rush, Mark S. Woodrey, Robert J. Cooper

Bibliographic record

VenueOrnithological Applications · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNest (protein structural motif)BayEstuaryMarshHabitatEcologyVegetation (pathology)Salt marshGeographyWetlandFisheryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

To document nest survival and habitat differences in the nesting habitats of Clapper Rails (Rallus longirostis) in tidal marshes of the northern Gulf of Mexico, we monitored 76 active nests within the Pascagoula River Marsh Coastal Preserve (a freshwater-dominated estuary) and the Grand Bay National Estuarine Research Reserve (a marine-influenced estuary) in coastal Mississippi from 2005 to 2007. During 2006, we measured the height of each Clapper Rail nest, sampled vegetation at active Clapper Rail nests and at random locations, and measured the distance to the nearest tidally influenced body of water and the average height and density of vegetation. Early in the breeding season, the average nest height was lower at the Pascagoula (36 cm) than at Grand Bay (60 cm), but, as the season progressed, nest height increased at the Pascagoula only. Within both estuaries, Clapper Rail nest sites were more structurally complex than at random locations and were associated with a greater diversity of vegetation. Overall, daily survival rates of Clapper Rail nests were relatively high (0.97–0.99), with the majority of nest loss apparently the result of tidal flooding. Our results suggest that where diverse habitat was available, Clapper Rails varied the height of their nests as a mechanism to avoid nest loss from tidal flooding. Habitat alteration from factors such as sea-level rise and coastal development may lead to lower nest success because of a loss of diverse nesting habitat.

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 score0.044
Threshold uncertainty score0.511

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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