Use of the noise-free interval (NFI) metric to assess the disturbances of airborne vessel noise at Glacier Bay National Park
Bibliographic record
Abstract
Alaska's Glacier Bay National Park preserves the seventh largest unit of the National Wilderness Preservation System, encompassing 2.6 million acres. Natural acoustic environments are significant to many of the unit's fundamental resources and values. Since 2001, the National Park Service has inventoried acoustic environments of Alaskan parks. One purpose is to document every noise-free interval (NFI) observed. NFI is defined as the time between human-generated noise disturbances. Aggregate properties of NFIs describe fragmentation of acoustic environments by noise. Median NFI estimated at parks in Alaska to date range from < 3 minutes to 16.7 hours, similar to other Arctic sites (0.5 to 13.0 hours, Stinchcomb et al. 2020). For the Glacier Bay Marine Management Plan Environmental Assessment, a geometric NFI model was developed using automatic identification system (AIS) derived vessel tracks. The NFI simulation results, along with NFI data from acoustic monitoring at the park, was then utilized to assess how changes to vessel quotas and vessel management strategies would potentially affect the NFI throughout the park. This paper will discuss the estimation and use of the NFI metric in protected natural areas, along with NFI modeling methods utilized for an environmental assessment at Glacier Bay.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".