Interval-based analysis of BOCR (benefits, opportunities, costs and risks) models evaluated by multiple experts
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
This paper presents a quantitative decision making methodology for evaluating best alternative using benefits, opportunities, costs, and risks (BOCR) models together with the interval computation. The quantification using BOCR-interval arithmetic modeling is performed in association with two types of models: analytic network process (ANP) and analytic hierarchy process (AHP) via consensus of multiple experts. The former is illustrated as BOCR-ANP in the form of a control network, whereas the latter BOCR-AHP as a strategic hierarchy network. We apply interval arithmetic operations — addition, subtraction, multiplication and division on the results obtained from BOCR-ANP/AHP models evaluated by different experts. This has been illustrated with an example of constructing a quantitative model for preserving the high quality of eggs and increasing the farm management effectiveness in the context of Salmonella Enteritidis (SE) outbreak.
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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 | 0.000 | 0.000 |
| 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 it