Determining the speed dependent source level of a snowmobile traveling on sea-ice
Bibliographic record
Abstract
As part of the Sustainable Nunatsiavut Futures project, a field experiment to determine the acoustic properties and underwater radiated sound level of a snowmobile was designed and executed. The fieldwork consists of lowering acoustic recorders under the sea ice and driving a snowmobile with a known position and velocity to evaluate its speed-dependent source level. This experiment is the first step toward collaborative Dalhousie and community research on underwater sound as it relates to the marine habitat, human use of the ocean, and sea-ice in Nunatsiavut. Due to COVID-19, the planning stages were coordinated virtually, and the fieldwork in Nunatsiavut was conducted by local Inuit Research Coordinators (IRCs), while a twin Dalhousie-led experiment was conducted in Caraquet, New Brunswick. A single hydrophone sensor was used in Nunatsiavut, and a vertical array of hydrophones was used in Caraquet to obtain underwater sound data from a moving snowmobile. Skidoo specifications for each site were recorded as well as sea-ice thickness, temperature, salinity, and sound-speed data were collected. Spectrograms of skidoos traveling at different speeds were computed. Comparisons between received levels at different velocities, sites, and ranges are shown, and the impact of sea ice and snowmobile specifications on received levels are discussed.
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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 | 0.000 | 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) | 0.002 | 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".