Comparing nondestructive measures of forage structure and phytomass
Bibliographic record
Abstract
Nondestructive radiative transfer and canopy volume methods were compared with the destructive hand-clipping method to determine forage structure and phytomass. On a native range site, fifteen 1-m2 circular plots were located at each of five microsites. On a crested wheatgrass site, thirty 1-m2 plots were located in grazed and in ungrazed areas. At peak standing crop, all plots were measured with a LI-COR Plant Canopy Analyzer to determine leaf area index (LAI), diffuse non-intercepted radiation (DNIR), and mean tilt angle (MTA) of leaves. Then, plants within plots were measured with a ruler to determine volume. Finally, all phytomass within plots was harvested. At the native range site, plant volume was related with LAI and DNIR on four of five microsites. Phytomass was related with LAI and DNIR on two microsites. At the crested wheatgrass site, volume and phytomass were related with LAI, DNIR, and MTA on grazed plots. Only phytomass was related with LAI and DNIR on ungrazed plots. The Plant Canopy Analyzer measures canopy structure and phytomass; it is fast, and its data are transferred directly to a computer. Measuring plant volume is inexpensive and requires minimal training. Determining phytomass by clipping is accurate and requires minimal training, but it is time-consuming and destructive. Key words: Leaf area, canopy, volume, phytomass, radiative transfer
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".