Joint measurements of NDVI and crop production data-set related to combination of management zones delineation and nitrogen fertilisation levels
Bibliographic record
Abstract
This research compared four nitrogen (N) management strategies (uniform N rate: UR, variable N rate based on crop proximal sensing: VR-PS, variable N rate based on management zones: VR-MZ and variable N rate based on integrating crop sensing and MZ: VR-PSMZ), evaluating their effect on maize grain yield, partial factor productivity (PFP N ), and net return above N fertiliser cost (RANC). The study provided a practical tool for choosing the fertilisation strategy that best performs in each agro-environment. These datasets are a supplementary material to the research paper by [3]. Data were collected over seven site-years experiments conducted in North-Eastern Colorado (USA). In dataset 1, for each site-year, data includes geo-referred points where grain yield and Normalised Difference Vegetation Index (NDVI) were measured, each one associated with its respective N rate, management zone (MZ), PFP N , RANC, and N management strategy. In order to group the observations reflecting homogeneous crop vigour, NDVI values were clustered within NDVI classes. In dataset 2, the main soil properties measured in several geo-referred points in each location are provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".