Variations in chemical composition of grass clover mixtures over the vegetation season in different agroecological conditions
Bibliographic record
Abstract
The aim of the current investigation carried out on 6 family farms in 6 counties was to determine seasonal variations in chemical composition of domestic and Canadian grass-clover mixture (GCM). The study was arranged as complete randomly design on 1 ha area at each of the family farm involved (0.5 ha domestic, 0.5 ha Canadian GCM). The sward at each family farm was cut five times over the vegetation season. GCM samples were analyzed by NIR spectroscopy (NIR instrument, Foss, Model 6500) to determine: dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), metabolic energy (ME), digestibility of OM in DM (D-value) and water-soluble carbohydrates (WSC). No differences in chemical composition between the two GCM were observed, but among locations for DM, OM and WSC. The interaction of mixture x location was significant (P<0.05) only for OM. The investigated parameters showed no significant differences of the mixture x cutting interaction (P>0.05). This means that no significant differences were noticed between mixtures at the same term of cutting, but for all of the investigated parameters among cuttings. There quality of GCM was increasing over the season as seen from its relative increase in CP from the first (112.9 g kg-1) to the fifth cut (185.9 g kg-1). NDF was higher over the whole vegetation season. The last cut showed significantly lower NDF (628.6 g kg-1), higher ME (10,73 MJ kg-1), D-value (71.6) and WSC (56.3 g kg-1).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".