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Record W2119046745 · doi:10.1093/beheco/arr002

Hummingbirds choose not to rely on good taste: information use during foraging

2011· article· en· W2119046745 on OpenAlexaff
Ida Elizabeth Bacon, T. Andrew Hurly, Susan D. Healy

Bibliographic record

VenueBehavioral Ecology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsForagingBiologyTasteMealSensory systemCognitionResource (disambiguation)Food choiceQuality (philosophy)Food scienceEcologyComputer scienceNeuroscienceMedicine

Abstract

fetched live from OpenAlex

To increase their chances of survival and reproduction, animals must detect changes in food quality and then decide if, and how quickly, to adjust their behavior. How quickly an animal responds to change will depend on the information available (cognitive, sensory, or physiological) and how it weights those types of information. Surrogate measures of meal size suggest that sensory information is used to make initial choices about how much to eat following changes in resource quality, choices are subsequently altered and refined as further information becomes available. Using direct measures, we investigated the amount of food consumed, the time taken to feed, and the interbout intervals between visits to a feeder of rufous hummingbirds, before and after changes in sucrose concentration. The hummingbirds did not change how much they drank at first experience of a new concentration but then rapidly adjusted meal sizes toward optimal for that concentration over a few feeding visits. Thus, it seems the hummingbirds used both cognitive and physiological information to decide how much to drink but appeared to ignore sensory information, such as taste. The early responses animals make to changed resources enable us to determine the types of information on which they rely most in their decision making.

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 score0.514
Threshold uncertainty score0.536

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 communication0.0000.001
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 head0.076
GPT teacher head0.268
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

Citations18
Published2011
Admission routes1
Has abstractyes

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