doi:10.1155/2011/161574 Research Article A Direct Assessment of “Obesogenic ” Built Environments: Challenges and Recommendations
Bibliographic record
Abstract
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper outlines the challenges faced during direct built environment (BE) assessments of 42 Canadian communities of various income and urbanization levels. In addition, we recommend options for overcoming such challenges during BE community assessments. Direct BE assessments were performed utilizing two distinct audit methods: (1) modified version of Irvine-Minnesota Inventory in which a paper version of an audit tool was used to assess BE features and (2) a Physical Activity and Nutrition Features audit tool, where the presence and positions of all environmental features of interest were recorded using a Global-Positioning-System (GPS) unit. This paper responds to the call for the need of creators and users of environmental audit tools to share experiences regarding the usability of tools for BE assessments. The outlined BE assessment challenges plus recommendations
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.742 | 0.602 |
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".