Under-ice noise generated from diamond exploration in a Canadian sub-arctic lake and potential impacts on fishes
Bibliographic record
Abstract
Mineral exploration is increasing in Canada, particularly in the north where extensive diamond mining and exploration are occurring. This study measured the under-ice noise produced by a variety of anthropogenic sources (drilling rigs, helicopters, aircraft landing and takeoff, ice-road traffic, augers, snowmobiles, and chisels) at a winter-based diamond exploration project on Kennady Lake in the Northwest Territories, Canada to infer the potential impact of noise on fishes in the lake. The root-mean-square noise level measured 5 m from a small diameter drill was approximately 46 dB greater (22 kHz bandwidth) than ambient noise, while the acoustic particle velocity was approximately 40 dB higher than ambient levels. The loudest sounds at the exploration site were produced by ice cracking, both natural and during landing and takeoff of a C130 Hercules aircraft. However, even walking on the snow above the ice raised ambient sound levels by approximately 30 dB. Most of the anthropogenic sounds are likely detectable by fishes with hearing specializations, such as chubs and suckers. Other species without specialized hearing adaptations will detect these sounds only close to the source. The greatest potential impact of noise from diamond exploration is likely to be the masking of sounds for fishes with sensitive hearing.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".