Innovative consumers: ecological, behavioral, and physiological predictors of responses to novel food
Bibliographic record
Abstract
Abstract Consumer innovation, that is, the acquisition and consumption of novel food types, has received little attention, despite its predominance among animal innovations and its potential implications for the ecology and evolution of species in a changing world. Results of the few studies that have investigated individual responses to novel foods suggest that various ecological, behavioral, and physiological variables may affect individual propensity for consumer innovation, but further work is needed to clarify these relationships. We investigated whether urbanization, social rank, exploratory personality, and baseline levels of corticosterone predict food neophobia and consumer innovation responses of wild-caught black-capped chickadees (N = 170) from 14 sites along an urbanization gradient. Our analyses do not support a link between food neophobia or consumer innovation and urbanization, dominance, or exploratory personality. However, birds with higher levels of baseline corticosterone were quicker to contact novel food types, and more likely to consume novel foods than individuals with lower levels of the hormone. This finding suggests that physiological states that promote foraging behavior might drive individual responses to novel food. Additionally, we found that chickadees tested later in autumn were less neophobic than those tested earlier in the season, perhaps reflecting seasonal changes in food availability. Together, the ability of baseline corticosterone and date of capture to predict responses to novel food suggest that necessity may drive consumer innovation in chickadees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".