Bibliographic record
Abstract
Harbour porpoises ( Phocoena phocoena ) are often sighted at the Canaport Liquid Natural Gas (LNG) terminal at Saint John, NB. High noise levels are thought to displace porpoises. Porpoise presence was studied when three oil tankers docked, offloaded cargo and embarked between Nov 14 and 25, 2014. An automated porpoise detector (C-POD) and an underwater sound recorder (Song Meter SM2) were deployed from a trestle, <100 m from the stern of the ship while docked. Broadband levels (0.1- 16 kHz) of 1 min recordings every 10 min were matched to the presence or absence of echolocation click detections over 10 min periods (n = 1527). Porpoise presence dropped from 49 ± 50 % (n = 1134) to 21 ± 41 % (n = 393) when tankers were present. Porpoise hearing thresholds are high at low frequencies. The insensitive low frequency hearing abilities can be taken into account when audiogram values are used as a frequency weighting function (expressed as dB(Pp); analogous to dB(A) for humans). The highest noise broadband (0.1 – 16 kHz) level when a porpoise and a tanker were both present was 107 dB(Pp) re 1 uPa (147 dB re 1 uPa unweighted). No porpoises were detected when the sound levels were between 107 and 120 dB(Pp) re 1 uPa (n = 29). The low frequency insensitivity of harbour porpoise hearing would reduce the likely perceived noise levels by 50-70 dB at frequencies where the ship noises have the greatest amplitude. The regular, although reduced, presence of porpoises when tankers are present suggests that they will tolerate moderate noise levels and related disturbances, but it does not indicate if the porpoises are physiologically stressed or not. Linear broadband noise level measurements should not be used when assessing the potential impact of noise levels on porpoise behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".