Active Sensor Reflectance Measurements of Corn Nitrogen Status and Yield Potential
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
Active sensor reflectance assessments of corn ( Zea mays L.) canopy N status are advocated to direct variable N applications and improve N use efficiency (NUE). Our goals were to determine: (i) growth stage and (ii) sensor vegetation index with greatest sensitivity in assessing N status and grain yield. Variable crop N was generated by supplying N at different amounts and times in three field studies. Chlorophyll meter (CM) and sensor data were gathered at two vegetative (V11 and V15) and two reproductive (R1 and R3) growth stages, using the Crop Circle sensor that measures reflectance in visible (590 nm) and near infrared (NIR) (880 nm) bands. Sensor data were converted to the normalized difference vegetation index (NDVI 590 ) and chlorophyll index (CI 590 ) values. Grain yields were also determined. Sensor indices were more highly correlated with CM readings for vegetative vs. reproductive growth ( r 2 of 0.85 vs. 0.55). The CM vs. CI 590 slope was over twice the NDVI 590 slope value, indicating CI 590 was more sensitive than NDVI 590 in assessing canopy greenness. Indices did not differ in ability to distinguish yield variation. Results indicate sensor CI 590 values collected during vegetative growth are best suited to direct variable N applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it