Bibliographic record
Abstract
Southern Ontario residents are faced with many challenging decisions when growing their own food. The intention of my research is to help these urban residents plan their garden plot in order to yield food for their own use. The form of this research will be a thesis incorporating a decision support system (DSS). This DSS is intended to take in and determine relevant site characteristics (latitude, sun/shade conditions) and use this information to help the user choose a variety of vegetables and herbs. Users will have the option of making a simplified model of their property and nearby structures for shade analysis, and with the results select an appropriate area(s) of their land. This DSS will give the user the freedom to pick vegetables based on conditions and preferences and give graphical and tabular output of the garden layout and details. The objectives of this thesis is to present the why, what, who, where, and how of going beyond local food production for urban consumption to urban citizens growing their own food for themselves. This food can be consumed but also used as a currency with which to barter for other yard produce from neighbours or community members. One could imagine having a bartering relationship with a neighbour or having a weekly or monthly food market to facilitate bartering. This DSS is intended to be one of the building blocks of a food network DSS, which would be used to increase the efficiency of sharing food produced in urban residential gardens (that have been planned using the following DSS prototype).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".