Acoustic monitoring of pile driving activities at the proposed NaiKun wind farm site.
Bibliographic record
Abstract
Underwater sound level measurements were obtained during marine pile driving activities at the proposed NaiKun wind farm site in Hecate Strait, British Columbia, Canada. Three hollow steel piles 0.9 m in diameter were driven into the seafloor to secure in place a truss to support a meteorological instrumentation mast. The activities involved both vibro-hammering and impact hammering. Measurements were collected at 10 m range to fulfill regulatory requirements for real-time monitoring of the near-source sound pressure levels and also at a selection of longer ranges to allow a characterization of the propagation conditions of the environment. A bubble curtain was utilized to mitigate the underwater noise generated by impact hammering after trial measurements over a few hammer strikes showed that unmitigated levels exceeded a regulatory threshold. Results from this study were used to derive source level estimates for a subsequent sound propagation modeling study conducted as part of the environmental assessment process. Results from the real-time monitoring at 10 m from the source (with and without the use of the bubble curtain) and from measurements obtained between 100 m and 3 km range will be presented.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".