Supplementary material to "Review Article: Multi-criteria decision making for flood risk management: a survey of the current state-of-the-art"
Bibliographic record
Abstract
Were multiple stakeholders included in the MCDM process?Which participatory technique was applied?Sensitivity analysis was performed?Which sensitivity analysis method was used?Uncertainty analysis was performed?Which uncertainty analysis method was used?Tkach and Somonovic 1997 A new approach to multi-criteria decision making in water resources Journal of Geographic Information and Decision Analysis Canada alternative ranking SCP, CP No No No Buzolic et al. 2001 Decision support system for disaster communications in Dalmatia International Journal of Emergency Management Croatia emergency management PROMETHEE No No No Margeta and Knezic 2002 Selection of the flood management solution of Karstic Field Water International Croatia alternative ranking AHP, PROMETHEE I, PROMETHEE II Yes does not mention No No Azibi and Vanderpooten 2003 Aggregation of dispersed consequences for constructing criteria: the evaluation of flood risk reduction strategies European Journal of Operational Research France alternative ranking WSM Yes group meeting No
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.030 |
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".