Remote Sensing Technique to Recognise Physical Characteristics of Water Bodies of the Republic of Kazakhstan
Bibliographic record
Abstract
Kazakhstan has been facing a significant reduction in its renewable freshwater resources due to various anthropogenic factors (climate change, agricultural practices, industrialization, urbanization). The reduction of renewable freshwater has led to water scarcity, desertification, health impacts, economic impacts. The relevance of the subject matter is determined by the importance of remote sensing of water bodies of the Republic of Kazakhstan using modern methods of satellite biometrics to form objective ideas about the real state of the country's water resources. The purpose of this paper is to study the currently existing effective methods for recognising the physical characteristics of water bodies of the Republic of Kazakhstan using remote sensing. The methodology of this research work is based on a combination of methods of system analysis of the possibilities of remote sensing of water bodies using satellite bathymetry with an analytical study of methods for recognising the physical characteristics of water bodies of the Republic of Kazakhstan by remote sensing. The results obtained during this research indicate the high accuracy of the remote sensing method using satellite bathymetry and the feasibility of its practical application in the future for the successful solution of similar problems.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".