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Record W2111500542 · doi:10.1093/beheco/arq031

Both the past and the present affect risk-sensitive decisions of foraging rufous hummingbirds

2010· article· en· W2111500542 on OpenAlexaff
Ida Elizabeth Bacon, T. Andrew Hurly, Susan D. Healy

Bibliographic record

VenueBehavioral Ecology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsForagingPreferenceAffect (linguistics)BiologyEnergy budgetOptimal foraging theoryConstant (computer programming)Variable (mathematics)EcologyStatisticsPsychologyMathematicsComputer science

Abstract

fetched live from OpenAlex

There is substantial evidence that an animal's current energy budget affects its preference for food patches that provide a constant reward relative to patches that provide a variable reward, when both patches have the same mean reward. Animals currently on a positive energy budget are expected to choose the constant option, whereas animals on a negative budget are expected to use the variable option. There is increasing evidence that prior experience can affect an animal's current decisions. We investigated choices made by rufous hummingbirds when they were tested with strong or weak sucrose solutions after several days of foraging on those strong or weak solutions. Foraging from weak concentrations prior to and during testing led to a higher preference for the variable option, whereas foraging from strong concentrations led to an increased preference for the constant option. We suggest that the energetic conditions experienced by animals prior to testing had a significant impact on the animals’ risk-sensitive decisions, and their memories of those prior conditions may have played an additional role. This implies that the conditions animals are maintained under prior to testing may significantly affect the outcome of risk-sensitivity experiments.

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)0.001
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 score0.335
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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
Published2010
Admission routes1
Has abstractyes

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