The Tooty Fruity Vegie project: A collaboration between education, health, the food industry and the horticultural industry.
Bibliographic record
Abstract
Inadequate intakes of fruits and vegetables are consistently and strongly associated with increased incidences of many cancers, cardiovascular and coronary disease (Joshipura et al 1999, Ness 1997, Block et al 1992). Despite this, most Australian adults' diets fall well below recommended daily intakes (Stickney 1994, CSIRO 1993). Whereas intake levels appear adequate among pre-schoolers, they become more inadequate as children get older (ABS 1995, AHC 1996). Because of this, as well as evidence that dietary habits formed early in life tend to continue into adulthood (Auld et al 1988, Gutlin 1990), we developed a fruit and vegetable promoting intervention for primary schools. We used existing knowledge, the successes, failures and lessons learned, from similar Australian and overseas projects (Gortmaker et al 1999, Reynolds et al 2000, Baranowski et al 2000, Story et al 2000, Stafford 1997, Contento et al 1995, NHF 1997), as well as broader health promotion and behaviour change theories (St Leger 1993, WHO Ottawa Charter 1986). The result was the Tooty Fruity Vegie (TFG) project, a two-year multi-strategy program, which ran in ten primary schools during 1999 and 2000.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".