The Use of Spatial and Non-Spatial Information Systems/Analyses for Predicting Crop Dynamics in the Nile Delta, Egypt
Bibliographic record
Abstract
The objectives of this paper are to develop a new approach to crop-dynamics prediction, based solely on geomatics technology, and secondarily, to apply this approach in order to monitor future crop dynamics in the Nile Delta of Egypt. The pattern of crop green-up is examined by statistical analysis NOAA NDVI (Normalized Difference Vegetation Index) satellite imagery. The analysis focusses on November 1998 to April 1999, a winter-crop calendar. It also examines the sustainability of land to support various crops, that is, is cropping land area being decreased or increased over time. Crop coverage is predicted using a combination of logistic regression spatial analysis and Markov non-spatial analysis. The combined approach developed for this study capitalizes on the strengths of each technique—the Markov analysis was used to predict the actual number of landscape units (pixels) expected to show cropping pattern changes, while logistic regression was used to identify the spatial distribution of the Markov pr...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".