Using <scp>MACBETH</scp> for the performance expression of a <scp>mixed‐use</scp> ecopark
Bibliographic record
Abstract
Abstract The deployment, control, and continuous improvement of a sustainable industrial park are complex and cross‐sector endeavours involving many different aspects. Generally, control of a sustainable industrial park comprises a range of actions that are undertaken to achieve its sustainability that is deployed into fundamental objectives. Achieving these objectives requires the definition, recurring redefinition, and continuous control of an action plan. The decision maker in charge of the industrial park needs pieces of information on the impact of the action plan before and during its execution. The performance expressions (also called utilities), are evolving during the execution of the action plan and rely on multiple criteria since a sustainable industrial park is a complex system with numerous objectives. In this paper, an innovative use of multi‐criteria decision analysis is presented. MACBETH is used to express the evolution of the performance of a sustainable industrial park, either for the purpose of prediction or verification. A case study is presented with the expression of the performance of a Canadian sustainable industrial park.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".