Exploring the relationships between biomass production, nutrient acquisition, and phenotypic traits: testing oat genotypes as a cover crop
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
High biomass and nutrient acquisition are desirable for oat (Avena sativa L.) as a cover crop. However, our understanding of oat genotypes suitable for cover crops and associated traits is limited. The objectives of this experiment on growing oat as a cover crop, after winter wheat (Triticum aestivum L.) harvest, were to determine biomass production, nutrient uptake of a set of oat genotypes, and to identify phenotypic traits that can be used as indicators to select cultivars suitable for cover crops. The results showed that the top biomass-producing genotypes took up larger amounts of soil nutrients, up to 142 kg N ha−1 and 17 kg P ha−1 in 2016, and 43.5 kg N ha−1 and 8.3 kg P ha−1 in 2017. The biomass production was significantly related to plant height and leaf area index (LAI) in both years, and to the normalized difference vegetation index (NDVI) in 2017. Both NDVI and LAI were closely related to the total amounts of N and P uptake. The poor association between biomass and NDVI in 2016 was due to vigorous growth of volunteer wheat and weeds as well as severe rust (Puccinia coronata f. sp. avenae Eriks.) infestation. Our results suggest that it is important to choose oat varieties as cover crops. Leaf area index can be used as a nondestructive indicator for final biomass and nutrient acquisition, while both NDVI and LAI are important traits for choosing oats as soil conservation cover crops.
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 | 0.001 | 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 | 0.001 | 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