Mineralenmanagement onder de loep : Koeien & Kansen-bedrijven vergeleken met andere melkveebedrijve
Bibliographic record
Abstract
Mineralenmanagement is een belangrijk onderwerp op melkveebedrijven. Inzicht hebben in nutrintenstromen van en naar het bedrijf en in de interne nutrintenstromen helpt ondernemers om efficinter om te gaan met grondstoffen en om te blijven voldoen aan de stringenter wordende mestwetgeving. Deze rapportage geeft inzicht in het mineralenmanagement van voorlopers van melkveebedrijven (Koeien & Kansen-bedrijven) in vergelijking met andere melkveebedrijven voor de periode 1998-2014. K&K-bedrijven hebben over een langere periode gezien lagere fosfaatoverschotten per hectare en in het algemeen een hogere efficintie in de kringloopschakels veestapel en bodem. Vanaf 2008 realiseren de K&K-bedrijven niet langer lagere stikstofoverschotten dan andere melkveebedrijven. Mineral management is an important topic on dairy farms. Gaining insight into nutrient flows from and to the farm and insight into the internal nutrient flows helps entrepreneurs to use resources more efficiently and to continue to satisfy the increasingly stringent fertiliser legislation. This report provides insight into the mineral management of pioneers among dairy farms (Koeien & Kansen [Cows & Opportunities, K&K] farms) in comparison with other dairy farms for the period 1998-2014. Viewed over an extended period, K&K farms have lower phosphate surpluses per hectare and in general greater efficiency in the cycle links of livestock and soil. From 2008, the K&K farms have no longer been producing lower nitrogen surpluses than other dairy farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.009 |
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; both teacher heads 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".