Sensitivity and uncertainty analysis of model hypoxia estimates for the Texas‐Louisiana shelf
Bibliographic record
Abstract
Abstract Numerical ocean models are becoming increasingly important tools for marine research and for management of marine resources. It is therefore crucial that uncertainty in model predictions and model sensitivity to errors in the model inputs be quantified. We performed a combined sensitivity and uncertainty analysis for a realistic physical‐biological model of the Texas‐Louisiana shelf in the northern Gulf of Mexico. The model simulates the major physical and biological processes involved in the formation of the hypoxic zone that develops on the shelf every summer. With the help of a statistical emulator technique, we introduced uncertainty in selected model inputs and assessed the effects of these uncertainties on the predicted development and spatial distribution of bottom hypoxia. The uncertain inputs we examined belong to two categories: (i) biological inputs including river nutrient concentration, phytoplankton growth rate and initial and boundary conditions of biological variables, and (ii) physical inputs including freshwater river runoff, wind forcing, and mixing coefficients. We show that uncertainty in different inputs has distinct effects on model output which vary in magnitude, time, and space. Uncertainty in physical inputs was found to have a strong impact on estimates of hypoxia, e.g., hypoxic area estimates vary by more than 40%, due to a 20% variation in the freshwater river runoff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".