Characterization of First Cut Alfalfa and Grass Silage Management Practices on Canadian Dairy Farms
Bibliographic record
Abstract
Production practices for first cut alfalfa (ALF), alfalfa-grass (AFG) and grass silages (GRS) were surveyed on dairy farms in four Canadian regions, i.e. British Columbia (BC, n=21), Prairies (PRA, n=32), Central Canada (CEN, n=218), and Atlantic Canada (ATL, n=17). Results are presented as percentages of responses by region; forage type, silo type, wilting method and inoculant use varied among regions. In CEN (93.0%), ATL (88.2%), and PRA (68.8%) AFG was most common. In BC, GRS was most common (66.7%). ALF was only reported in the PRA (28.1%) and CEN (5.6%). Respondents from BC only reported bunkers (52.4%) and baleage (28.6%). Bunkers were most common in PRA (31.3%), followed by baleage (28.1%), piles (18.8%) and tower silos (6.3%). In CEN tower silos were most common (37.2%), followed by bunkers (33.9%), baleage (22.0%), and piles (1.8%). In ATL bunkers (29.4%) and baleage (29.4%) were most common, followed by tower silos (17.7%), and piles (11.8%). Wilting was mostly done in windrows: BC (55.0%), PRA (45.0%), CEN (77.1%), and ATL (71.0%). In BC and ATL, 45% and 17.7%, respectively, of respondents used tedders to enhance wilting. In BC, CEN, ATL, and PRA, 55%, 58.3%, 64.7% and 44.8% respectively of respondents used inoculants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".