Wat kunnen we leren van de Maya's voor de toekomst van onze landbouw? | MENU VAN MORGEN
Bibliographic record
Abstract
Met de overbevolking op aarde én de avocado- en quinoa-hype, wordt het de hoogste tijd dat we een lesje van de maya’s leren. Want alle innovaties daargelaten; de maya’s bleken fantastisch goed in staat in duurzame landbouw bedrijven. Wil je meer weten? Als je enthousiast bent geworden, kun je bij Kennislink meer over dit onderwerp lezen. Meer dan de helft van Nederland bestaat uit landbouwgrond. We verbouwen daarop zoveel dat we de tweede exporteur ter wereld zijn. De keerzijde daarvan is dat intensieve landbouw ten koste gaat van de biodiversiteit. In Boxtel bedachten ze een duurzaam alternatief: op de Herenboerderij komt de natuur terug.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.192 |
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".