PERFORMANCE OF SPECTROGRAM CROSS-CORRELATION IN DETECTING RIGHT WHALE CALLS IN LONG-TERM RECORDINGS FROM THE BERING SEA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We investigated the performance of spectrogram cross-correlation for automatically detecting North Pacifi c right whale (Eubalaena japonica) calls in long-term acoustic recordings from the southeastern Bering Sea. Data were sampled by autonomous, bottom-mounted hydrophones deployed in the southeastern Bering Sea from October 2000 through August 2002. A human analyst detected right whale calls within the fi rst month (October 2000) of recorded data by visually examining spectrograms and by listening to recorded data; these manual detections were then compared to results of automated detection trials. Automated detection by spectrogram cross-correlation was implemented using a synthetic kernel based on the most common right whale call type. To optimize automated detection parameters, the analyst performed multiple trials on minutes-long and hour-long recordings and manually adjusted detection parameters between trials. A single set of optimized detection parameters was used to process a week-long recording from October 2000. The automated detector trials resulted in increasing proportions of false and missed detections with increasing data set duration, due to the higher proportion of acoustic noise and lower overall call rates in longer recordings. However, the automated detector missed only one calling “bout ” (2 or more calls within a 10-minute span) of the 18 bouts present in the week-long recording. Despite the high number of false detections and missed
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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 it