Mid term evaluation of the Bavarian agri-environmental programme. Effects of the KULAP-A on soil and water /доклад на 87 семинаре ЕААЕ, Assessing Rural Development Policies of the CAP, Vienna, Austria, 21-23 April 2004
Bibliographic record
Abstract
In the context of the mid-term evaluation of rural development programmes (EU regulation 1257/99) the Bavarian agri-environmental programme, the so-called Kulturlandschaftsprogramm Part A” (KULAP-A), was evaluated. By means of this programme, only agricultural land related measures are supported. The measures may refer to the whole agricultural enterprise, parts of it or the individual plot. The guideline of the EU-commission demand analysis about the effects of the programme on biotic and abiotic environmental resources such as soil, water, species and their habitat and landscape. This paper focuses on the results concerning the protection of soil erosion and water contamination. For this, statistical data corresponding the supported measures were analysed. Additionally a survey of farmers participating and non-participating at the programme was conducted. In this study the dead-weight-effect is discussed as an effect which can be excluded in cases when: due to the programme an intensification of management is prevented, a low intensive management is reached or the abandonment of farming could at least be delayed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".