Adaptation of a crop composition and configuration analysis method to European agricultural landscapes
Bibliographic record
Abstract
Agricultural landscapes approximately occupy a large part of available land surtace and as such constitute a keystone in biodiversity conservation programs. ln return, biodiversity contributes ta production through ecosystem services as pollination or pest contrai. However, recent studies also suggested that new policies and modifications in agricultural practices in order to promote biodiversity must be accepted by the different actors of the rural space and incorporated in existing practices. ln this perspective, deciphering the raie of agricultural landscape heterogeneity in maintaining biodiversity may be a promising research direction. The FARMLAND project precisely aims at giving an answer to this questions combining tools from geomatics, remote sensing and geostatistics associated to ecological research on biodiversity. Based on spatial indexes from landscape metrics, this work proposes a mapping method for sampling quadrats and constituting an experiment design in order to dissociate the influence of composition (the number and probability of occurrence of the different caver types) and configuration landscape heterogeneity (the spatial display of caver types) on biodiversity. Afler the sampling of quadrats in each of the 7 European sites, multi-taxa biodiversity records and ecosystem services identification will be undertaken. We present here the different steps (1 ta 7) of the adaptation of the initial methodology from a previous study (Pasher et al., 2011) ta the French site "Vallées et Coteaux de Gascogne" that constituted a test zone for the other European sites.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".