Bibliographic record
Abstract
The geographical area that is the province of Manitoba stretches across five distinct terrestrial ecozones, each with a unique combination of land forms, soils, water features, vegetation and climate. Within each ecozone exists a number of ecodistricts characterized with distinctive relief, geography, landforms and soils, vegetation, water, fauna and land use. The majority Manitoba’s agricultural activity, including pig production, lies within the Prairies and southern portions of the Boreal Plains ecozones. Agriculture activity exists to a lesser degree in the northern Boreal Plains and Boreal Shield ecozones, however, industry and public initiatives are likely to increase agriculture, including animal production in these regions. The challenge to Manitoba regulators is great as environmental regulation or policy requires the flexibility to address issues across this broad and geographically diverse landscape. Trends over the past 20 years confirm that Manitoba’s agriculture activity is diverse and remains very dynamic. Pig production has played a major role in this evolution and today leads the agriculture sector in farm cash receipts. However, unlike many other pork producing regions of the world, Canadian farm investment and management practices have two to four fold greater reliance on export markets. The challenge is to identify environmental protection policies that continue to offer Manitoba’s agriculture sector, and the pig industry specifically, opportunities to adapt as market forces change and new technologies become available.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.855 | 0.762 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".