Bibliographic record
Abstract
I examined song variation within and among 23 individual Buff-breasted Flycatchers (Empidonax fulvifrons) recorded in the Chiricahua and Huachuca mountains of Arizona in 1999. I recorded two distinct song types from each individual during intense pre-dawn singing. I used both spectrographic cross correlation (SPCC) of entire songs and discriminant function analysis (DFA) of temporal and frequency measurements to examine whether songs were individually distinctive, and whether songs differed between the two localities. Similarity values of pairs of songs from SPCC were significantly greater for within-male than for between-male comparisons for both song types. Mean similarity values for the two song types did not overlap between these comparison categories. Similarity values between songs of pairs of males from the same mountain range were not greater than for comparisons between pairs of males from different ranges. All temporal and frequency measures for both song types varied significantly more among than within individuals. DFA of principal component scores derived from these measures assigned 85% of Type 1 and 86% of Type 2 songs to the correct individual. Only three frequency variables measured from Type 1 songs differed significantly between birds from the two mountain ranges. DFA assigned only 61% of songs of either type to the correct mountain range, not significantly greater than expected by chance. Thus, both techniques demonstrate significant individual distinctiveness in songs of this species, and neither suggests any geographic structuring of song variation between the two mountain ranges. However, SPCC is considerably more efficient and has greater potential to assign unknown recordings to known individuals correctly, and to detect recordings of “new” individuals not included in the reference sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".