Applying uncertainty principles in environmental modelling
Bibliographic record
Abstract
The authors implemented a causal probability network (CPN) model using the HUGIN shell to represent part of the overall effect of acid precipitation on lakes. A tool for entering raw data into the CPN model, based on earlier rule-based modeling efforts by the RAISON project at Canada Centre for Inland Waters, Environment Canada, was developed. The network editor developed allows the water chemistry and flow data to be directly applied to calculate marginal distributions ready for input to HUGIN. The authors used the RAISON experience to draw preliminary conclusions concerning environmental models which have representation and propagation of uncertainties in hypotheses and outcomes. The authors show the utilization of Bayesian inference to the watershed aggregates defined for the earlier, rule-based model developed for acid rain. Naturally occurring and industrial causes of aquatic acidity are incorporated in a network with probabilities generated from observations. A sample CPN with four nodes is illustrated. It represents the relationship between water chemistry, color, sulfate load, and the percent reduction of acid neutralizing capacity in a lake.>
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".