6. Organic and Conventional Agriculture: Assessing Synergies Between Agricultural Approaches
Bibliographic record
Abstract
Organic agriculture (OA) and conventional agriculture (CA) represent two polar approaches to farming, both of which hold their own challenges and implications with the impending global food crisis. One of Canada’s major exports include crops, and yet globalization coupled with climate change present pressing agricultural issues leading us to ask how our farming methods will adapt to feed the world’s burgeoning population. An approach to finding a solution can come from setting aside the principles and biases defining organic and conventional farming to find a combinatory approach to farming, assuming that they are not so dichotomous they can be combined. A survey of three major Canadian crops (wheat, corn, canola) and agricultural variables relevant to food production and climate change (crop yield, emissions, energy usage, and application of fertilizer) in OA and GA will lay out a spectrum upon which an optimized combined approach to farming can be sought. Ultimately, this project aims to reconcile OA and GA farming practices in the best interests of human well-being and the environment when considering the predicted global food crisis from a Canadian perspective.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".