Bibliographic record
Abstract
North Atlantic, North Pacific, and southern right whales all produce the up call, a frequency-modulated upsweep in the 50-200 Hz range. This call is one of the most common sounds, and frequently the most common sound, received from right whales, and as such is a useful indicator of the presence of right whales for acoustic surveys. A data set was prepared of 1857 calls and 6359 non-call sounds recorded from North Atlantic right whales (Eubalaena glacialis) near Georgia and Massachusetts. Two methods for the detection of the calls were compared: spectrogram correlation and a neural network. Spectrogram correlation parameters were chosen two ways, by manual choice using a sample of 20 calls, and by an optimization procedure that used all available calls. Neural network weights were trained via backpropagation on 9/10 of the test data set. Performance was measured separately for calls of different signal-to-noise ratio, as SNR heavily influences the performance of any detector. Results showed that the neural network performed best at this task, achieving an error rate of less than 6%, and is thus the preferred detection method here. Spectrogram correlation may be useful in situations in which a large set of training data is not available, as manual training on a small set of examples achieved an error rate (26%) that may be acceptable for many applications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".