The building blocks of ranging: What laboratory studies of distance perception can tell us
Bibliographic record
Abstract
Ranging (estimating the distance to) conspecifics is an important skill for songbirds. For example, territorial males must determine, often by acoustic information alone, whether a rival male is either within territorial boundaries, requiring an aggressive response or outside boundaries, requiring him to withhold response to conserve energy. Most field studies of ranging in songbirds measure males’ behavioral responses to playback of vocalizations seeming to originate from within or outside an established territory. However, this approach is inadequate for investigating ranging abilities in species that do not defend territories or for assessing the extent to which ranging abilities are dependent on early experience with distance. In a series of studies, a go/no-go operant task requiring birds to discriminate vocalizations recorded at various distances was developed to ask some of these comparative questions about auditory distance perception. Results showed that a territorial species, black-capped chickadees (Poecile atricapillus), learned to discriminate more quickly than a nonterritorial species, zebra finches (Taeniopygia guttata), that both species learned to discriminate chickadee vocalizations more quickly, and that chickadees raised without experience with ranging could perform the distance cue discrimination task as well as field-reared birds. [Research completed at Queens University, Kingston, Canada; supported by NSERC.]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".