Viability of Myrtle tree as natural filter for the gaseous emissions of internal combustion engines
Bibliographic record
Abstract
This paper was aimed to test the control role of myrtle tree against the gaseous emissions of stationary internal combustion engines (ICEs). CO and NO2 gaseous emissions, chlorophyll content index (CCI) and leaf surface area were studied prior and after the expose of myrtle tree the exhaust of 2 KW gasoline fueled, power generator that was operated four hours per day for a period of 24 consecutive weeks. Myrtle have shown efficient performance in reducing the amounts of these emissions, where records of CO and NO2 have shown reductions to about 18% and 27% of their initial levels as emitted from the source, respectively. Although it was not encouraging at the first few weeks, the CCI has shown significant development of 38% as compared to its initial value, which was incorporated with about 77% increase in average leaf`s surface area. Statistical analyses have proved good positive correlations between CO and NO2 removal process from one side and the CCI and leaf surface area from the other. Atmospheric temperature was proved to have high negative correlation coefficient with both CCI and leaf surface area. These results encourage further biological and statistical tests to prove and determine the causal relations between these variables. Author would like to acknowledge the support of the Institute of International Education IIE, Scholars Rescue Fund SRF and Russ College of Engineering, Ohio University.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".