On some aspects of data integration techniques with environmental applications
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 Multiple Criteria Decision Making (MCDM) is a popular phrase used to describe situations where there is a need for integration of the results of different studies to make an overall judgement. Among the highest priorities towards socioeconiomic development around the world is the Environmental Protection Policy (EPP), and environmental assessment is a key to EPP. In the context of environmental studies, data integration techniques are very appealing and have wider applicability. It is well known that land, air and water are the three sources for determination of the extent of pollution of different regions. The purpose of MCDM is to rank the regions wrt all the sources taken together. For any individual source of pollution, it is trivial to rank the regions from best to worst. However, the problem of integration becomes non‐trivial in most cases since the regions do not lend themselves to the same pattern of ranking wrt different sources. In this article we examine critically the performance of two popular composite indices (CI) and suggest some alternatives. Copyright © 2003 John Wiley & Sons, Ltd.
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.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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