Uncertainty propagation in environmental decision making using random sets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Significant uncertain information is involved in environmental decision making due to complexities of natural systems, lack of sufficient data, and the interpretation of information that may be in numerical or linguistic forms. Uncertainties can be present in identification of criteria, interactions among criteria, evaluations of alternatives, eliciting weights from experts, and the choice of aggregation operators. Uncertainties arising from performance evaluations of criteria for each alternative and weights can be identified as aleatory (random) and epistemic (informal and lexical) uncertainty. These two types of uncertainty were best respectively represented as probability density function and possibility distribution. A methodology was presented in this paper to propagate these two kinds of uncertainty through aggregation operators. Random set theory is used as a uniform framework to integrate aleatory uncertainty and epistemic uncertainty. Evidence theory is utilized to approximate the probability measure when both probability density functions and possibility distributions are transformed into random sets. This methodology facilitates the incorporation of aleatory and epistemic information into the multicriteria environmental decision makings.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it