Bibliographic record
Abstract
Hoe vernieuwt de Nederlandse land-en tuinbouw?Innovatie en vernieuwing in de Nederlandse landbouw is belangrijk voor de versterking van de concurrentiekracht en het realiseren van beleidsdoelstellingen ten aanzien van duurzaamheid.Het ministerie van Landbouw, Natuur en Voedselkwaliteit (LNV) voert beleid om innovatie en vernieuwing in de land-en tuinbouw te bevorderen.Situatie LNV streefde naar minimaal 10% innoverende bedrijven in de land-en tuinbouw in 2015.De streefwaarde geldt voor het totaal van innoverende bedrijven en vroege volgers.Motieven voor en belemmeringen bij innovaties vaststellen, zodat beleidsmakers daarop kunnen inspelen.Wat is het aandeel vernieuwende bedrijven in de land-en tuinbouw?Welke motieven hebben ondernemers om te vernieuwen?Uitdaging
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".