A survival model of the effects of bottom-water hypoxia on the population density of an estuarine clam (<i>Macoma balthica</i>)
Bibliographic record
Abstract
The effect of bottom-water hypoxia on the population density of the clam Macoma balthica is estimated using a survival-based approach. We used Bayesian parameter estimation to fit a survival model to times-to-death corresponding to multiple dissolved oxygen (DO) concentrations assessed from scientific experts. We describe guidelines for ensuring the accuracy of such assessments and claim that elicitation of quantities that pertain to measurable variables of interest, rather than unobservable parameters, should improve the use of judgment-based information in Bayesian analyses. When directly relevant data are lacking, predictions based on subjective assessments can serve as the basis for preliminary management decisions and additional data collection efforts. To inform pending water quality controls for the Neuse River estuary, North Carolina, we combined the survival model with a model describing the time dependence of DO. For current conditions, the mean summer survival rate is predicted to be only 11%. However, if sediment oxygen demand (SOD) is reduced as a result of nutrient management, summer survival rates will increase, reaching 23% with a 25% reduction in SOD and 46% with a 50% SOD reduction. Full model predictions are expressed as probabilities to provide a quantitative basis for risk-based decision-making and experimental design.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".