Sectorrapportage Duurzame Zuivelketen : Prestaties 2020 in perspectief
Bibliographic record
Abstract
Through the Sustainable Dairy Chain initiative, dairy companies and dairy farmers are jointly working on a future-proof and sustainable dairy sector. In 2011, the Sustainable Dairy Chain formulated targets for 2020, relating to climate-neutral development, continuous improvement of animal health and welfare, preservation of grazing, and protection of biodiversity and the environment. This sector report shows to what extent these targets were achieved in 2020. This report also addresses the targets set by the Sustainable Dairy Chain in 2019, for the period up to 2030. Five 2020 target themes were achieved in 2020. These themes are: responsible antibiotics use, energy efficiency, grazing, responsible soy, and dairy cattle phosphate excretion. For four themes, the targets were not achieved. These are: greenhouse gas emissions, ammonia emissions, renewable energy production and dairy cow lifespan. However, progress was made on the latter two themes in 2020. The production of sustainable energy increased for the fourth year in a row and dairy cow lifespan increased for the second year in a row. A monitoring system for animal welfare and biodiversity was created, but no baseline measurements for 2020 have been carried out and no sector targets have been set as of yet. In 2020, the number of dairy cattle and the number of young stock increased by 1.0% and 1.6%, respectively, compared to 2019. This was unfavourable for the outcome of themes like greenhouse gas and ammonia emissions.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".