Effects of the environment and culture depth on growth and mortality in juvenile Pacific oysters in the Strait of Georgia, British Columbia
Bibliographic record
Abstract
The effects of environmental variables, culture depth, and phytoplankton abundance and composition on juvenile Pacific oyster Crassostrea gigas growth and mortality were studied between June and October of 2008 at 4 sites in the Strait of Georgia, British Columbia, Canada.In addition, the effects of temperature-triggered depth manipulation on growth and mortality of oysters were examined in order to assess potential control measures for mitigating high summer mortalities associated with high temperature, harmful algal blooms (HABs), and other environmental stressors.Control oysters were held at constant depths of 3, 10, and 15 m, while experimental oysters were kept at 3 m depth and lowered to 10 or 15 m when the surface water temperature reached 14, 16, or 18°C.Site and Depth significantly affected the growth and mortality of control oysters.At the site with the best growth, cumulative mortality was low (range: 6.4 to 19%) and negatively correlated with temperature and positively with transparency.At the high-mortality site (range: 64 to 98%), mortality was positively correlated with temperature, chlorophyll concentration, and the biomass of diatoms and potentially harmful algae.Cumulative mortality was generally higher at 3 m than at 15 m depth.Significantly larger oyster volume was obtained with the oyster controls at 3 m than with those held at 10 or 15 m at most sites.Temperature-trigger treatments did not significantly affect oyster volume or cumulative mortality, and oysters moved to 10 and 15 m had final volumes similar to the 10 and 15 m controls, independent of trigger temperature.Oyster growers could select their sites for maximal growth and minimal mortality based on temperature profile, freshwater input, and phytoplankton abundance and composition.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".