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Record W1965155807 · doi:10.1139/z06-087

Flight speeds of seven bird species during chick rearing

2006· article· en· W1965155807 on OpenAlexvenueno aff
Keith Chan, Robert W. Blake

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsForagingBiologyRange (aeronautics)Animal scienceTangentWingEcologyMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Flight speeds of seven bird species were recorded using a hand-held Doppler radar for adult birds flying to and from foraging areas while rearing chicks. R.A. Norberg (1981. J. Anim. Ecol. 50: 473–477) predicted that birds rearing chicks should fly at speeds greater than the maximum range speed to bring the most food to their chicks as long as the associated increase in travel costs can be more than compensated for by foraging in the travel time saved. From aerodynamic and total power curves based on a range of literature values for the drag coefficient (0.05–0.4), the minimum power speed (minimum point on the U-shaped curve), and maximum range speed (a tangent to the curve from the origin at which the distance traveled per unit energy is maximized) are compared with the mean measured flight speed for each species. For all species, the mean measured flight speed was significantly less than the maximum range speed (p < 0.05), which is independent of foraging style and habitats, suggesting that flying at speeds greater than the maximum range speed may not be a practical strategy for birds rearing chicks.

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)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.184
Teacher spread0.177 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

Explore more

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