Bibliographic record
Abstract
One of the main environmental issue these days is acid rain and its effects on the human and environment. Acid rain effects are more dominant in countries such as United States, Canada, and Europe due to acidic nature of soils in some parts of their lands as well as heavy pollution resulted from vast industrial activities which are conducted through these countries. Contrary to these facts, Iran situation regarding acid rain is totally different and in spite of high pollution in cities and industrial areas, the water of lakes and streams are not acidic. Data collected in this research show that the pH and alkalinity of the lake water and soils are almost high. Some of the lakes in Fars County are dried and the rest are not in normal situation. Lakes of Barmshoor, Droodzan, and Haftbarm have pH around 7.93 -8.07 and alkalinity around 186 to 220 mg/L CaCO3. The soils around the lakes have pH in the range of 7.69-7.89 and alkalinity 208-235 mg/L CaCO3. Therefore both the soil and the water have high alkaline buffer capacity to resist acid rain because; most part of the Fars County consist of calcite, dolomite and some alkaline salts. Pollution load indexes for Al, Zn and Cu for both lake water and related soils are close to one (1.063-1.54) which means no considerable metal pollutions are created by acid rain in Fars County. In fact, high pH and alkalinity of the water and soil make metal salts mostly insoluble and limit the availability of the free metals. The pH changes of rain water show gradual increase of pH during raining. If the sample of rain water is left alone, its pH decreases by residence time.
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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) | 0.001 | 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".