Optimal size and filling of dog walking areas
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
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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