Using structural equation modeling and expert elicitation to select nutrient criteria variables for south-central Florida lakes
Bibliographic record
Abstract
Kenney, M.A., G.B. Arhonditsis, L.C. Reiter, M. Barkley, and K.H. Reckhow. 2009. Using structural equation modeling and expert elicitation to select nutrient criteria variables for south-central Florida lakes. To protect the nation's waterbodies from excessive impairments from pollution leading to eutrophication, the Clean Water Act requires states to establish water quality standards. These water quality standards are designed to protect the designated use, or water quality goal; however, they are indirectly measured and assessed using a water quality criterion. An alternative approach to develop nutrient criteria is the predictive approach (CitationReckhow et al. 2005), which determines the predictive variables by combining water quality data with assessments from multiple experts on the probability of designated use attainment using structural equation modeling (SEM). Our objective was to expand the predictive approach to include a region of waterbodies and to use multiple experts. To demonstrate these extensions, the approach was applied to lakes in south-central Florida using four experts to quantify attainment of a fish and wildlife designated use. Multiple models were built that related eutrophication processes to the designated use. Of the two plausible models, total phosphorus was the most predictive of the designated use followed by chlorophyll a. Using the model results, the risk of nonattainment of the designated use for these two predictive variables was calculated; to achieve high attainment (90% or more), total phosphorus should be < 0.015 mg/L and chlorophyll a < 5 μ g/L. This study provides vital extensions to the previous approach through its use of multiple experts and a region of lakes, making the approach applicable to other regions of waterbodies and conclusions useful to inform policy.
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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 | 0.038 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".