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 The article focuses on one of the most often used GIS‐based multicriteria analysis methods: the weighted linear combination (WLC). The WLC model has traditionally been used as a global approach based on the implicit assumption that its parameters do not vary as a function of geographical space. This assumption is often unrealistic in real‐world situations. The article proposes a new approach to GIS‐based multicriteria analysis. It develops a local form of the global WLC model. The range sensitivity principle is used as a central concept for developing the local WLC model. The principle postulates that the greater the range of criterion values is, the greater the weight assigned to that criterion should be. Consequently, the local criterion weight can be defined for each neighborhood within a study area as a function of the range of criterion values in a given neighborhood. The range of criterion values provides also the base for defining the local value function. The article presents the theory behind the local WLC modeling and illustrates an implementation of the model in a GIS environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.021 | 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