YIELD AND FEATURES DETERMINING PRODUCT QUALITY IN SAMPLES OF ALFALFA COLLECTION
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
The research was carried out in order to identify collection samples of alfalfa (M. sativa) and variable (M. sativa x M. varia), promising for breeding in the south-east of Kazakhstan. Sowing was carried out in the spring of 2019, by a coverless method, calculations for 2019-2021. The material for the study was 134 varieties of various ecological and geographical origin, the standard is the local variety Semirechenskaya local. As a result of the study of genotypes, samples of alfalfa (M. sativa) showed good bushiness: (k-14) from the USA, (k-5677) Italy, (k-315) France, (k-5677) Italy, (k-5677) Italy, k-267) Uzbekistan, more than the standard for three years on average by 11-12 pieces. Whereas in alfalfa the following samples distinguished themselves: (k-39932) from Canada, (k-26713) Ukraine, (k-47492) Kazakhstan, (k-23206) Ukraine (k-34627) Kazakhstan, these samples exceeded the standard by an average of 3 – 5 pieces. In terms of foliage in alfalfa, the highest indicators were in samples: (k-45479) from Russia and (k-5677) Italy, as well as in alfalfa variables (k-31885) Russia, (k-33299) Canada, (k-39932) Canada, (k-61324) Kazakhstan. In both species of alfalfa, leafiness varied within 51.0 – 52.3%, the excess over the standard was 23.2-36.5%.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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