Bibliographic record
Abstract
Swath grazing is a practice for overwintering beef cows on small grain forage left in the field to graze through snow, but annual variation occurs. The objective is to review 18 years of research at Lacombe, Alberta (AB) to describe how production attributes are related to cost mitigation and cow performance. Five trials compared swath grazing management options to pen-fed controls to determine feasibility. Associated studies monitored forage quality, soil fertility requirements and planting date impacts on yield. Mean, standard deviation, coefficient of variation, maximum and minimum values (n=18) for forage yield, pasture days, carrying capacity and percent utilization were determined from annual treatment averages for small grain species used from 1997 to 2017. Swath grazing reduced total daily costs of winter feeding compared to pen feeding, but amounts varied from trial to trial. Savings were larger for feeding activities (yardage) of cows than feed production (feed), although both were reduced consistently. Small grains lost nutritive value over winter, cows lost weight in 3 of 5 trials, but reproductive performance matched the pen-fed practices. Forage yield and carrying capacity followed similar trends for magnitude, variability and were inversely related to feed cost. Low utilization and forage quality is related to cow weight loss, but low utilization may be more important. Lower savings could be associated with low yield and poor utilization caused by deep snow, muddy conditions, and forage freezing to soil. Planting date of all species was delayed to mature and swath late in Sep. to minimize impacts of weathering. Fertilizer-N requirements may be reduced in fields grazed repetitively, but soil nutrient accumulation could cause environmental problems and lodging.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".