Environmental Risk Assessment of Spring Floods in the Akmola Region of Kazakhstan
Bibliographic record
Abstract
The relevance of the subject matter in this study is conditioned by a high degree of risk of spring floods in Akmola region, significant damage to the economy of the region and the need to develop and implement effective measures to counteract this natural situation.The purpose of the study is to develop a proper assessment of the environmental risk of spring floods and their consequences in the flooded areas of Akmola region.The methodological approach in this study is based on a combination of system analysis methods.They analyse the degrOctober 2023ee of probability of various kinds of environmental risks caused by spring floods and investigate the regularities of development of negative consequences of floods as a whole and in Akmola Region of the Republic of Kazakhstan in particular.In the course of this study, findings have been made indicating the serious consequences of spring floods for economic activity in the areas affected by seasonal flooding of water bodies.The findings also indicate the need to develop and implement specific measures to prevent spring floods caused by serious disturbances to economic activity in water protection zones.The findings of this study and the scientific conclusions drawn from them are of significant practical significance for agricultural workers in Akmola region, who have to cope with the consequences of spring floods in the area where they operate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".