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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
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; both teacher heads agree on what is shown here.
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".