Evaluation of national farmers' registry data in geo- information context: Case study of Trabzon, Turkey
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
For the management of agricultural subsidies, information on farmers and farmland in Turkey is registered in the National Registry of Farmers (NRF) system. However, the system currently does not include any integrated spatial data. This hinders the population of necessary information on the actual agricultural land and thus, makes it impossible to correlate farmers’ declaration with the actual agricultural land use. In this study, NRF data in two pilot areas in the province of Trabzon, Turkey were evaluated using digital cadastral data and ortho photo/image (ortho products). For the evaluation, the actual land use patterns of study areas were extracted from ortho products and then the areas of actual land use patterns were compared with the corresponding areas in the registries. As a result, it was determined that nearly 70% of the actual agricultural land was not registered in the NRF system. In addition, parcel based comparisons between registries and corresponding actual land use pattern uncovered considerable un-systematic anomaly between the reality of agricultural land use and farmers’ declarations. It is suggested that the current system should be further developed in terms of geo-spatial data by integrating digital cadastral data and ortho products. Key words: Digital cadastre data, national registry of farmers, ortho photo/image, agricultural subsidy, spatial data.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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