Time-space distribution of North Atlantic Right Whale in Gulf of St. Lawrence from acoustic monitoring between 2010 and 2022
Bibliographic record
Abstract
Recordings from the passive acoustic monitoring (PAM) network deployed in the Estuary and Gulf of St. Lawrence for detecting marine mammals and assessing ocean noise are analyzed to extract the time-space pattern of habitat use by North Atlantic right whale (NARW) from 2010 to 2022. Data from an ensemble of 24 027 days of observation, recorded at 12 seafloor PAM stations and 8 ocean observing (OOS) buoys, were processed to detect NARW upcalls using an artificial intelligence (AI) algorithm previously developed for this ecosystem. NARW occurrences in this marginal inland sea of the Northwest Atlantic during the ice-free period, shifted in 2015 from occasional to frequent. High occurrence levels were maintained since. Although NARW upcalls were detected over a large part of the Gulf, they were very rare out of the southern Gulf shelf and north-northwest of Anticosti Island. Excluding rare presence events, the average occurrence season began at the end of April and ended at the beginning of December. The majority of annual occurrences (90% of occurrence days) was between 5 June and 2 November. The NARW occurrences into the Gulf from our PAM network culminated in mid-August. The established general spatial pattern then persisted until November. It comprised two main areas: the Southwestern shelf of the Gulf of St. Lawrence, where most of the occurrences were observed, and the area north-northwest of Anticosti Is., where the occurrence levels were lower. The occurrences in Shediac trough dominated throughout the season. Variability of occurrences at the stations within and between season was common. The proportion of seasonal occurrence in the Gulf throughout the season is analyzed to infer the seasonal mean pattern of NARW incursion and retreat, and to extract relevant dates for NARW protection and management decisions.
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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 | 0.000 | 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 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".