Offsetting increases in underwater noise
Bibliographic record
Abstract
Offsetting is a management tool applied to address habitat impacts that are not fully mitigated by other actions. A proposal to offset anticipated increases in underwater noise was developed by Fish and Fish Habitat Protection Program (FFHPP, the client), with application to TMX vessel-related underwater noise in relation to southern resident killer whales (SRKW). Advice was sought on the exchange of offset credits, which are defined both spatially and temporally. This included: the use of sub-regions for credit exchange; the formation of a baseline, and calculation of changes in sound levels resulting from project-related vessels and mitigation actions; the use and calculation of weighting factors to express variable habitat importance to focal species; and the principles guiding credit exchange as part of offsetting. The application of offsetting to address underwater noise is unprecedented and complex. Such an approach has potential value for addressing underwater shipping noise, but is data intensive, and is not recommended in data-poor settings. Challenges and methodological concerns were identified in the applicability and effectiveness of the proposed approach as a management tool. While the analysis completed could inform the choice of mitigation measures, additional work is required before proceeding with implementation of the proposed noise offsetting approach (including for TMX and SRKW), due to large uncertainties that have been identified. Offsetting should be constrained by the area of occupancy of the focal species and occur in spatial and temporal proximity to noise increases. Comparability in the importance and type of the habitat to the focal species is required when considering credit exchange; however, the criteria for biological equivalency were not agreed upon. The proposed sub-regions for offsetting credit exchange in the test case were not agreed upon, and contributed to the rejection of the approach. Suggestions were provided for a more rigorous approach to spatial subdivision. Refinements to the vessel noise model presented in the test case were suggested. Careful consideration of the selection of sound level metrics, appropriate frequencies, source levels, and propagation assumptions is required, including spatial and temporal considerations. Reservations were expressed regarding the identification, calculation, and exchange of offsetting credits. As presented, the use of offset credits was rejected, and improvements were suggested to support future considerations. Participants agreed that consideration of a multiplier or risk factor (the ratio between the impacted and compensated habitat) may be applied as a management tool to capture uncertainties of the proposed approach, any periods of time without offset, or any other relevant considerations. The baseline should be established to represent a period in time prior to project operations, and prior to implementation of project inputs, mitigation measures, and offsets, consistent with the principle of additionality.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".