On Individual, Sex and Age Differentiation ofIndian House Crow (Corvus splendens) Call: APreliminary Study in Potohar, Pakistan
Bibliographic record
Abstract
Considering importance of acoustics studies in population biology, 500 calls of the Indian House Crow (Corvus splendens) were recorded in morning - mid-afternoon hours (January-February, 2009) from 23 sites of urban areas of Potahar (Punjab, Pakistan). Calls were recorded using Sony CFS 1030 S sound records (sampling rate = 48 KHz) and edited using Sound Analysis Pro (Version 1.02). software using FFT method rate 50%, data window 9.27 ms, advanced window 1.36 ms. Through editing of calls, we selected 60 (37 ƃƃ, 17 ƂƂ, 6 Juvenile ƃƃ) good quality spectrograms for detailed analysis. Spectrograms were characterized by rapid frequency modulations using 6 (call pitch, mean pitch goodness, mean frequency of the calls, frequency of modulations, mean amplitude modulation, mean wiener entropy) acoustic parameters. Significance of difference was analysed using Multivariate and Discriminate Function Analysis. Calls could be assigned to correct individual in 10.8% males, 21.0% females, and 42.9% juveniles, which was significantly higher than percentage of correct classification per chance. Calls could be attributes to correct sex in 88.5% and to correct age group in 80.6% of cases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".