RESEARCH Field validation of seconda y Clary and Kestens International Journal of Behavioral Nutrition and Physical Activity 2013, 10:77
Bibliographic record
Abstract
Many studies have been performed to better understand the relationship between exposure to the foodscape – defined by Winson as “the multiplicity of sites where food is displayed for purchase and where it may also be consumed ” [1] – and nutrition-related outcomes (e.g. obesity or dietary intakes) [2]. For pragmatic reasons, secondary data sources listing food outlets rather than field observations have been used to assess characteristics of the foodscape [3]. Uncertainty about the validity of such data sources raises the issue of potential and possibly systematic errors of measurement [4,5]. Recently, work has been conducted to validate commercial [6-9], Internet-derived [7,10] or government [8,10-12] databases, mainly in the US, the UK and Canada. Based on the match
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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 | 0.203 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".