Comparative characteristics of grain classifications of soft wheat of Kazakhstan and major grain-producing countries
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
Soft wheat is one of the most important crops, grown in more than 130 countries. To date, one-fifth of the world's wheat, or about 150 million tons a year, is sold on international markets. In the world trade traditionally dominated by the US, Australia, Canada and Argentina. Kazakhstan, being on the 15th place in the production of wheat grain, is among the first ten exporters - in 2017 the country exported about 8 million tons to the amount of 1.5 billion dollars. USA, then, as potential export opportunities are much higher, as evidenced by annual carryover stocks at 3 million tons. According to experts, considerable differences in the classification of wheat grain used in Kazakhstan and in other countries participating in the grain market and the methods for assessing the technological dignity indicators (TDs) laid down in the classifications are a significant obstacle to increasing the export potential of wheat grains. In view of this, an analysis was made of grain classifications of wheat grains used in the most important grain producing countries, TD indicators were determined, methods for their evaluation, and differences were revealed. It is established that in countries that are stable in the quantitative and qualitative characteristics of grain, an insignificant list of TD indicators is adopted, while they characterize the physical quality and state of the grain, which may indicate a general suitability for grinding. It is determined that in Russia and Kazakhstan, in determining the contamination, such an indicator as dockage is not taken into account. Comparative tests of different methods of sampling and determination of contamination have been carried out, and correlation coefficients have been established between indicators of contamination determined by different methods.
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 | 0.000 | 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 | 0.001 |
| 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