The Underwater Soundscape of Minto Inlet, Northwest Territories, Canada
Bibliographic record
Abstract
Passive acoustic monitoring is a powerful tool for observing soniferous species in remote marine environments over long periods of time, which can inform conservation planning and wildlife management. In this study, we collected seven months of passive acoustic data from early February to early September 2019 in Minto Inlet, Northwest Territories, Canada, to examine the seasonal presence of four species of marine mammals and unidentified fish species, examine drivers of underwater sound levels, and quantify underwater noise from vessel traffic. Bearded seals were vocally present from mid-March to early July. Ringed seals were present in every month. Beluga whales were present from early July until September. Bowhead whales were detected from April to August, and fish were detected from February to July. Underwater sound levels were dampened by the presence of landfast sea ice and increased with wind speed. Increased bearded seal calls also caused increased sound levels. The only vessel detected was the research vessel that recovered the acoustic recorder. Underwater sound levels were much lower in Minto Inlet compared to other areas in the region, and it appears to be an important site for many marine mammals where forage fish species, primarily Arctic cod, are abundant at depth. These results are in line with Minto Inlet being identified as an important area by the community of Ulukhaktok. At present, anthropogenic impact on this area appears to be very low, but further monitoring is required to determine if any management measures are necessary to maintain these low levels.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".