Features and status of miniature indigenous germplasm of cattle- Malnad Gidda
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
As the threat to human life and health from fine particulate matter (PM<sub>2.5</sub>) increases globally, the life and health problems caused by environmental pollution are also of increasing concern. Understanding past trends in PM<sub>2.5</sub> and exploring the drivers of PM<sub>2.5</sub> are important tools for addressing the life-threatening health problems caused by PM<sub>2.5</sub>. In this study, we calculated the change in annual average global PM<sub>2.5</sub> concentrations from 2000 to 2020 using the Theil-Sen median trend analysis method and reveal spatial and temporal trends in PM<sub>2.5</sub> concentrations over twenty-one years. The qualitative and quantitative effects of different drivers on PM<sub>2.5</sub> concentrations in 2020 were explored from natural and socioeconomic perspectives using a multi-scale geographically weighted regression model. The results show that there is significant spatial heterogeneity in trends in PM<sub>2.5</sub> concentration, with significant decreases in PM<sub>2.5</sub> concentrations mainly in developed regions, such as the United States, Canada, Japan and the European Union countries, and conversely, significant increases in PM<sub>2.5</sub> in developing regions, such as Africa, the Middle East and India. In addition, in regions with more advanced science and technology and urban management, PM<sub>2.5</sub> concentrations are more evenly influenced by various factors, with a more negative influence. In contrast, regions at the rapid development stage usually continue their economic development at the cost of the environment, and under a high intensity of human activity. Increased temperature is known as the most important factor for the increase in PM<sub>2.5</sub> concentration, while an increase in NDVI can play an important role in the reduction in PM<sub>2.5</sub> concentration. This suggests that countries can achieve good air quality goals by setting a reasonable development path.
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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