Bibliographic record
Abstract
Enthusiasm for optimal foraging theory in the 1970s and 1980s stimulated much work on foraging by bees and, to a lesser extent, hummingbirds. These animals were assumed to be energetically stressed because they were nectarivorous, small, and dependent on costly forms of flight. Researchers sought to explain foraging in terms of movement patterns that saved energy. For example, Pyke (1981) derived movement rules both within and between inflorescences. He compared observed directions and distances of movements following departure from a flower to optimal predictions, under the assumption that the animals should maximize their net rate of energy intake. Pyke (and many others) also assumed that animals would have imperfect knowledge about their environments, particularly with regard to predictions as to what and where to find food in the future. In addition to using statistical rules (Pyke 1984), such animals should always sample in order to track an ever-changing world. Despite the power and appeal of this viewpoint, we now see growing evidence that simple rules and patterns alone cannot explain foraging in hummingbirds. Here, we review how learning and memory influence hummingbird foraging and how memory might affect the ways in which hummingbirds pollinate plants. Much of a hummingbird's diet is derived from the nectar of flowers that, in turn, rely on hummingbirds for pollination. These flowers frequently provide only a few mg of sugar daily (Kodric-Brown & Brown 1978). Hummingbirds therefore must visit many flowers on each feeding bout, transferring pollen among flowers in the process.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".