URBAN PLANNING MODEL OF WATERFRONT RECREATION ZONES IN THE ALTAI MOUNTAIN REGION
Bibliographic record
Abstract
Introduction: Over the past quarter of a century, the issue of urban development within regional settlement systems has not been a priority for most policy-makers and professionals. Much more attention has been focused on the issues arising from the expansion of major metropolises: Moscow, St. Petersburg, Yekaterinburg, Kazan, and several others (seven in total). In the meanwhile, the urban development of unique natural landscapes in other regions has been progressing on its own, without major supervision or proper attention from the professional community. For instance, the pristine land along Lake Baikal has undergone urban development without proper planning documentation; vast areas in the Irkutsk Region have been sold off for logging; and the Altai Territory and the Republic of Altai are seeing intensive development of unplanned recreation hubs. Purpose of the study: The study is aimed to create an urban planning model for unique natural landscapes. Methods: We used such methods as multi-factor analysis, photographic footage, opinion poll, and graphical modeling. Results: Out study results in an original model that illustrates the optimal location of new recreation hubs, mindful of preserving the unique environmental qualities of the natural landscape.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".