Bibliographic record
Abstract
В статье определяются оптимальные размеры площадок по выгулу собак для разных типов общественных пространств в городе. Определены принципы выявления оптимальных размеров на основе анализа зарубежной научной литературы и градостроительной документации, которые подходят для таких пространств, как сад квартала, сад микрорайона и парк планировочного района. С опорой на идеи Лорела Аллена [11] анализируется минимально необходимое наполнение площадок для выгула собак с учетом потребностей животных и их владельцев и радиуса обслуживания площадки. Сделан вывод о необходимости классификации площадок по размерам и оптимальному их расположению в условиях сложившейся городской застройки. The article determines the optimal sizes of dog walking areas for different types of public spaces in the city. The principles for identifying the optimal sizes are determined, based on the analysis of foreign scientific literature and urban planning documentation, which are suitable for such spaces as the garden of the quarter, the garden of the micro-district and the park of the planning area. Based on the ideas of Laurel Allen [11], the minimum required filling of dog walking areas is analyzed, taking into account the needs of animals and their owners and the service radius of the site. It is concluded that it is necessary to classify the sites according to their size and their optimal location in the conditions of the existing urban development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".