NEW RESOURCES FOR SMART FOOD RETAIL MAPPING. A GIS AND THE OPEN SOURCE PERSPECTIVE
Bibliographic record
Abstract
In this paper it is demonstrated that open-source GIS software may contribute to allow nonprofit organizations and local food retailers to strategically locate food shops. This impacts realtors and other businesses as well. Areas are covered and clients served avoiding food deserts and increasing security in the health sector (Barnes et al., 2016). The methodology demonstrates how mapping may be processed, allowing people to get a good understanding of the food distribution. Also, decision making at corporate level improves due to better connecting to local production and organic retailers and to better reach out to local consumption. A major consequence of this exercise is likewise to educate users on the negative impacts of food deserts on health and improve awareness supporting the design and integration of sustainable and healthy lifestyles (Vaz and Zhao, 2016). This novel proposal that combines spatial and locational data visualization (McIver, 2003), as well as sharing of information of healthy food retailers within the urban nexus (Morgan and Sonnino, 2010) engage communities actively to participate in the integration of new consumer behaviours and make them clearly expressed.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".