Bibliographic record
Abstract
The ability to selectively apply herbicides through the use of weed mapping and variable rate sprayers offers the potential to reduce harmful effects on the environment as well as to optimize producer profitability. Over the past 25 years, there have been numerous studies involving remote sensing and weed/crop discrimination. With the development of greater spectral and spatial resolution sensors and spectral mixture analysis techniques re-investigation of the discrimination of weed and crop species present in cultivated systems appears timely. In a laboratory experiment, the spectral separability of five weeds and two crops of economic importance on the Canadian prairies was investigated. Reflectance from the uppermost fully expanded leaves of field grown plants was examined over the range 350-2500 nm using a field spectroradiometer and an integrating sphere. The various plant species were not separable using the broad bands present on the Landsat TM satellite. However, the weed/crop species could be discriminated using reflectance in approximately 30 10-nm wide bands across the electromagnetic spectrum. Results suggest that hyper-spectral remote sensing could be used for weed/crop mapping and merits further study under field conditions.
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.
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".