Spatiotemporal variability of pH in coastal waters of New Brunswick (Canada) and potential consequences for oyster aquaculture
Bibliographic record
Abstract
Abstract There is a void in the knowledge of the acidification status of Eastern Canada's coastal waters. This knowledge is crucial to evaluating the threats posed to marine life, particularly oyster farming, a flagship of New Brunswick seafood production. In this study, we measured the temporal variability of pH and related environmental parameters in three bays of Northeastern New Brunswick. We also evaluated the potential impact of the observed pH levels on the Eastern oyster ( Crassostrea virginica Gmelin, 1791), based on the available literature on this species’ sensitivity to acidification. We investigated the presence of inherent cycles of pH with the Fourier transform and the spectral filtering technique. Our results show that pH is highly variable in the studied area, with values ranging from 7.31 to 8.90. A seasonal effect was apparent, as the pH fluctuations were set at the lowest level in winter when the cover of ice and snow on the bay was present. The spectral analysis revealed a clear semidiurnal tidal pattern of pH, this variable being inversely related to the water level in summer and directly related to it in winter. The spectral subtraction of all the tidal components allowed the detection of a circadian rhythm that was not in pace with the alternation between day and night but rather slowly drifted so that the pH troughs occurred at night during the full moon period. Short periodicities of circa 8 and 6 h also existed in two of the three bays. Based on current knowledge of C. virginica ’s sensitivity to acidification, this species’ recruitment, growth, and survival are unlikely to be impacted by the present pH levels in the studied area. However, further acidification might overcome the resilience of C. virginica , especially that of the larvae that are produced during the winter in commercial hatcheries.
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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 | 0.003 |
| 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) | 0.001 | 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".