Additional file 1 of Effect of a relative pricing intervention and active merchandising on snack purchases: interrupted time series analysis of a hospital retailer-led strategy
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. Nutrient Profile of Menu Items from QEII Retail Foodservices Outlets, 2019. List of items on offer during a single week atone retail outlet with corresponding food group and nutrient coding including provincial nutrient profiling system (NS Food and Beverage Nutrient Criteria, 2016) and retail price. Supplementary Figure 1. Map of QEII Health Sciences Centre. Map not to scale; shows the buildings within the corresponding physical campuses and neighbourhood geography. Retail outlets in this study are located in buildings 1a (Large Cafeteria A; Grab-and-Go Café), 3 (Small Cafeteria), and 9 (Large Cafeteria B). Reproduced with permission from QEII Foundation 2022 https://www.nshealth.ca/sites/nshealth.ca/files/qeii-building-finder-map-colour.pdf. Supplementary Figure 2. Snacking Made Simple Merchandising Campaign Branding at Nova Scotia Health, 2019. Supplementary Figure 3. Interrupted time-series showing the impact ofa relative pricing intervention on total sales revenues ($CAD) at four retailfood sites in Halifax, Nova Scotia, from April 2018 – Dec 2019. Baseline = weeks 1-66;Intervention = weeks 67-87, commencing at the dotted line. Shading indicates atemporally matched subset corresponding to the calendar year segment during andprior to the intervention, during baseline.
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.
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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.813 | 0.076 |
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".