Using Canopy Hyperspectral Reflectance to Predict Root Biomass Carbon and Nitrogen Content
Bibliographic record
Abstract
Full assessment of soil carbon (C) and nitrogen (N) pools is necessary for long-term sustainability of agricultural production and provides information on plant health and nutrient cycling. A major component of nutrient cycling is plant root C and N. Although root C and N contribute to nutrient cycling, determination of these quantities is laborious and tedious and is, therefore, not commonly done. In this study we attempt to determine the feasibility of using remotely sensed canopy reflectance as a proxy to determine root C and N data of live, standing forages. The study site was the United States Department of Agriculture-Grazinglands Research Laboratory located in El Reno, Oklahoma. Twelve plots in each of two sites (a native, tallgrass prairie and an improved, Old World Bluestem pasture) were used for collection and measurement of root C and root N and measurement of canopy reflectance using a field portable hyperspectral spectroradiometer. Root and soil samples were then taken from under the remote sensed area for total C and N analysis using the combustion method. The results of this study indicated that it is feasible to predict root C and N, but further study is required to improve model accuracy.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".