Potential Discrimination of Toxic Industrial Chemical Effects on Poplar, Canola and Wheat, Detectable in Optical Wavelengths 400–2450 nm
Bibliographic record
Abstract
This research examined the spectral response of poplar (Populus deltoides, Populus trichocarpa), wheat (Triticum aestivum), and canola (Brassica napus) leaves subjected to fumigation with gaseous phase toxic industrial chemical gases (TICs). The gases include ammonia (NH3), sulphur dioxide (SO2), hydrogen sulphide (H2S), chlorine (Cl2), and hydrogen cyanide (HCN). This study aimed to determine if: (1) vegetation subjected to TICs could be distinguished from background vegetation during varying growth stages and environmental stresses; and, (2) different TICs could be distinguished based on the spectral response of vegetation. The results showed that both environmental and TICs induced similar spectral features inherent to plants, which are related primarily to chlorophyll and water loss. These features include pigments in the visible and cellulose, lignin, lipids starches, and sugars in the SWIR. Although no specific spectral features could be tied to individual TICs an analysis of the data using vegetation indices showed that the TICs and environmental stresses result in diagnostic trends from healthy mature to highly stressed leaves. In addition combinations of specific indices could be used to distinguish the effects of NH3, SO2, Cl2and their effect from that of other treatments of the study. The continued goal for this research program is to develop a remote detection capability for hazardous events such as a toxic gas leak. Our findings at the leaf level suggest that damage can be detected within 48 hrs and should last for an extended period. Thus, the next experimental step is to test if the results shown here at the leaf level can also be detected with airborne and satellites systems.
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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 | 0.000 | 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) | 0.001 | 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".