Problèmes de l’agriculture marginale dans la zone pionnière de l’Est du Canada
Bibliographic record
Abstract
A marginal farm can be defined by three criteria : the amount of improved land, the amount of time spent by the operator off the farm, and the absolute and relative income derived from agriculture. Both human and physical factors explain the deficiencies of marginal agriculture in the study area. The settlers' lack of agricultural experience, improper agricultural specializations, and the small size of the parcels of improved land (and consequently of the cattle herds) are the important human factors. The main physical limitations are climatic (especially the cool summer temperatures and the short length of the frost-free period), and pedologic. More research is needed before political decisions can be made concerning the future of these areas of marginal agriculture. Surveys of the marginal farms and studies of the physical limitations to agriculture should be made not only by economists and pedologists, but also by geographers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".