Farm Sustainability Assessment using the IDEA Method. From the concept of farm sustainability to case studies on French farms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although many indicator sets have been developed to characterise sustainability, a lack of available methods and operational tools to assess the sustainability of a farm is often reported. The use of specific indicators can be an interesting response if farmers themselves can use them in a process of self-assessment. First, the French IDEA method (Indicateurs de Durabilité des Exploitations Agricoles) of farm sustainability indicators illustrates the scientific approach adopted by authors to translate the concept of farm sustainability into a system of 41 sustainability indicators covering the three dimensions of sustainability. Secondly, some results are presented from different case studies illustrating tests of the IDEA method. Thirdly, the way of building the indicators is discussed on the basis of some results and of feed back from the users As a conclusion, a recent work linking the IDEA method with the national data bases is mentioned.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it