Monitoring of temporal trend of atmospheric metals by moss Hypnum cupressiforme Hedw in Jammu (India)
Bibliographic record
Abstract
Atmospheric pollutants, such as metals, are extremely variable in space and time and it is cumbersome and expensive to deduce detailed information over a vast area using traditional instruments. Consequently, a cost-effective bio-monitor was preferred in the present study to analyze the atmospheric metal load. For this purpose, a moss Hypnum cupressiforme Hedw was inducted amongst moss species available to evaluate the intensity and trend of atmospheric deposition of Zn, Cu, Cd, and Pb in Jammu in the state of Jammu and Kashmir, India for a period of three years, 2014- 2016. The biomonitoring experimentation was performed by transplanting moss bags prepared from Hypnum cupressiforme, after validating its tolerance against metals using a photosynthetic efficiency analyzer in the field. Moss Hypnum cupressiforme Hedw exposed seasonally, covering summer, monsoon, and winter periods, upon the analysis provided time-integrated patterns of metal bioavailability at the study sites. An attempt was made to compare the seasonal variations during the three years of study and findings exhibited significant seasonal variations in metal. The gradient of the metal load was in the order of Cu>Cd>Zn>Pb. The total average percentage increase in the metals under consideration, Zn, Pb, Cu, and Cd, was 40%, 37%,79%, and 76%, respectively during three subsequent years. It is worth mentioning here that biomonitoring studies on atmospheric metals have never been explored for this region.
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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) | 0.000 | 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 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".