Effect of Windrow Inversion on Hay Drying and Losses
Bibliographic record
Abstract
Mowing-conditioning alone is not enough to adequately dry high quality hay windrows in Eastern Canada conditions. Young leafy forages tend to pack down in the windrow and dry very slowly (Savoie et al., 1984). Additional mechanical handling is necessary to fluff or turn the windrow. The hay tedder increases the drying rate by about 40 % on the day it is applied (Pattey et al., 1988). Tedding losses per application are about 0,5 % in grass but can be higher than 4 % in alfalfa (Savoie, 1988). In addition, close to 75 % of the alfalfa losses are composed of the nutrient rich leaves. Recently, a few North American manufacturers have developed a windrow inverter to increase the drying rate while handling the windrow very gently. The main objective of this study was to investigate, under field conditions, the effect of windrow inversion on the drying rate and losses in both grass and alfalfa hay. Another goal of the research was to identify the best time to apply the hay windrow inverter.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".