Adaption potential of <i>Crassostrea gigas</i> to ocean acidification and disease caused by <i>Vibrio harveyi</i>
Bibliographic record
Abstract
Abstract The survival and development of bivalve larvae is adversely impacted by ocean acidification and Vibrio infection, indicating that bivalves need to simultaneously adapt to both stressors associated with anthropogenic climate change. In this study, we use a half-dial breeding design to estimate heritability (h2) for survival to Vibrio harveyi infection and larval shell length to aragonite undersaturated and normal conditions in laboratory-reared Crassostrea gigas. Phenotypic differences were observed between families for these traits with heritability estimated to be moderate for survival to V. harveyi challenge (h2 = 0.25) and low for shell length in corrosive (Ωaragonite = 0.9, h2 = 0.15) and normal conditions (Ωaragonite = 1.6, h2 = 0.15). Predicted breeding values for larval shell length are correlated between aragonite-undersaturated and normal conditions (Spearman r = 0.63, p < 0.05), indicating that larger larvae tend to do better in corrosive seawater. Aquaculture hatcheries routinely cull slow-growing larvae to reduce and synchronize time taken for larvae to metamorphose to spat, thus inadvertently applying size-related selection for larger larvae. This indirect selection in the hatchery populations provides a plausible explanation why domesticated oyster populations are less sensitive to ocean acidification.
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 | 0.000 | 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) | 0.001 | 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".