ENVIRONMENTAL RISK FACTORS of flooding in Pakistan and compare it with the situation in Iran
Bibliographic record
Abstract
Considering the exponential growth of scientific, technical and industrial in different countries including European and American, and in some cases lack of attention to environmental issues and an infection caused by the above-mentioned activities can be seen that the bold and the significant role that these countries play in global environmental consequences. It is worth noting that countries in emissions play a significant role. As massive floods in Pakistan, fires in Russia, lethal heat in Japan and severe climate change in Canada and Western Europe, have all been the result of stopping the flow of the jet stream over these areas, undoubtedly, if the necessary measures are not done in this regard, Iran will also undergo such incidents. Of the main reasons for floods in Pakistan can be warming and rapid climate changes, and also stipulates if that trend continues, the remaining glaciers will be melt, Pakistan will face in the future with far more critical conditions. The causes of the devastating floods in Pakistan, is the growing industrialization in developed countries, which is the cause of largest environmental pollution to other countries as well. Despite all laws and international conventions signed and ratified by these countries on the prevention of environmental pollution that it follows universally damaging effects of continued lack of compliance control authorities and we are witnessing the non-compliance by authorities and always this non-compliance swiped third world countries that are developing in good coverage.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".