Development of statistical models for prediction of the neurotoxin domoic acid levels in the pennate diatom Pseudo-nitzschia multiseries utilizing data from cultures and natural blooms
Bibliographic record
Abstract
Domoic acid (DA), a neurotoxin implicated in amnesic shellfish poisoning episodes, is produced by the pennate diatom Pseudo-nitzschia multiseries and its presence is associated with natural blooms of this marine microorganism. Laboratory studies indicate that production of the toxin by the diatom is related to physiological stress caused by limitation of nutrients, such as phosphate and silicate. This study attempts to develop predictive models of the amount of DA present, using data from laboratory physiological studies of the diatom, and from a combination of the laboratory data with field data from one Atlantic and two Pacific Ocean sites between 1988 and 1998. Such models can provide early warning of a potential poisoning episode without the sophisticated chemical determinations of domoic acid and avoid delays due to lack of analytical facilities and expertise. Predictor variables include nutrient ratios and cell abundance of Pseudo-nitzschia multiseries, determinations of which are both simple and inexpensive. Both linear and logistic regression methods are employed. Natural logarithm and square root transformations of predictors and response variables are needed to linearize the modeled relationship. In total, four models are proposed. Three of these models have r 2 ranging between 0.60 to 0.80 and are based on between four and seven predictors. Split sample reliability testing indicated that shrinkage on cross-validation for one of these models was below 35%. The fourth, a logistic regression model developed using the split-sample approach, has 2 predictors. In the training data, the prediction sensitivity and specificity of this model were approximately 76%. In the Validation data, the specificity increased to 87%, while the sensitivity declined to 67%.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".