Bibliographic record
Abstract
Categorization is the basic process of sorting discriminably different exemplars into classes or categories.Zentall, Galizio, and Critchfield (2002) expanded this to say that categorization is the process of determining what things belong together and a category is a group or class of stimuli orThe authors summarize progress in research on how songbirds (oscines) categorize the acoustic communication of conspecifics.They found that category perception for the learned songs and calls of oscines are well described by four principles: The exemplars from a single vocal category are discriminated one from another.Exemplars of different vocal categories are more easily discriminated than exemplars of the same category.Vocal categorization transfers to novel exemplars.Lastly, the labels applied to sets of vocal exemplars are descriptive of the natural categories an oscine species uses to classify exemplars.The authors use bioacoustic data to generate statistical predictions about the importance of vocal features; field and laboratory tests confirm the importance of those features.In comparisons between the study of visual and auditory categorization tasks, the authors suggest that auditory tasks are more useful because (i) human photography and its reproduction are a poor match for avian visual systems, and (ii) real-world experience with conspecific vocalizations impacts auditory classification in later operant discriminations.Finally, the authors consider the enmeshing of prototypes and exemplars in the representation of learned vocalizations and conclude that evolution provides prototypes used in species recognition and that experience provides exemplars used to recognize individual conspecifics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".