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Habitat differences in allocation of eggs between successive breeding attempts in great tits (<i>Parus major</i>)

2004· article· en· W2540186778 on OpenAlexvenueno aff
Marko Mägi, Raivo Mänd

Bibliographic record

VenueEcoscience · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsParusHabitatEcologyNest (protein structural motif)FacultativeBiologyAvian clutch sizeDeciduousReproductionPopulationForaging

Abstract

fetched live from OpenAlex

:The effect of habitat heterogeneity on animal behaviour and reproduction has recently captured serious attention in population and conservation biology. The great tit (Parus major) is a facultative double-brooded species that prefers deciduous forests as breeding habitats. However, it is able to reproduce in managed coniferous forests where nest boxes are provided. During 1999–2002, we measured various reproductive parameters of great tits, including double breeding, in a heterogeneous habitat complex consisting of both deciduous and coniferous forests. Probability of laying a second clutch after the hatch of the first clutch did not differ significantly between habitats. However, clutch size was allocated between two successive breeding attempts more equally in coniferous than in deciduous forests. To our knowledge, this is the first time that such a difference in seasonal breeding patterns between adjacent habitats has been demonstrated in birds. However, the total number of offspring fledged per pair did not differ significantly between habitats. Possible proximate and ultimate causes of the observed habitat differences are discussed. We suggest that habitat-specific allocation of reproductive investment between successive breeding attempts plays an important role in optimizing breeding tactics by facultative multiple-brooded bird species in a heterogeneous environment, potentially serving as a useful mechanism facilitating their adaptation to novel habitats.

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 score0.009
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.261
Teacher spread0.241 · 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

Citations34
Published2004
Admission routes1
Has abstractyes

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