Farm sustainability data for better policy evaluation with FADN
Bibliographic record
Abstract
FLINT delivers a data-infrastructure providing up to date information to policy-makers and the agro-food sector about farm level indicators on sustainability and other new relevant issues.FLINT establishes a pilot network of at least 1000 farms (representative of farm diversity at EU level, including the different administrative environments in the different MS) that is well suited for the gathering this kind of information.Taking into account the sustainability performance of farms on a wide range of relevant topics will facilitate better decision making.These topics include (1) market stabilization; (2) income support; (3) environmental sustainability; (4) climate change adaptation and mitigation; (5) innovation; and (6) resource efficiency.The approach will explicitly consider the heterogeneity of the farming sector in the EU and its member states.In cooperation with the farming and agrofood sector, the feasibility of these indicators will be determined.FLINT addresses the increasing needs for sustainability information of the national and international retail and agro-food sector.The Sustainable Agriculture Initiative Platform and the Sustainability Consortium -in which the agro-food sector actively participates -support the FLINT approach.The lessons learned and recommendations from the empirical research conducted in 9 purposefully chosen MS is used to estimate and discuss effects in all 28 MS.This will be very helpful in case the European Commission should decide to upgrade the pilot network to an operational EU-wide system.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.025 |
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".