The use of lichens as indicators of ambient air quality in Southern Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The inverse relationship between arboreal lichen species richness and sulphur \ndioxide in ambient air has been thoroughly documented in the literature. Previous \nwork in southern Ontario has shown that lichen bioindication can identify areas of \npotential concern regarding air quality. The EMAN suite of l i chens was applied in the \nCity of Samia by surveying 458 Sugar Maple trees, in order to test the applicability of \nlichen bioindication under conditions of high mean S02 levels and high species \nrichness values. The results of the survey were explored using Geographic \nInformation Systems. A spatial relationship between lichen community variables, the \nBluewater Bridge and the highway was identified. Lichen species richness, lichen \npercent cover and Index of Atmospheric Purity values were higher along the bridge \nand highway. No strong gradients were found between other known pollution sources \nand no lichen deserts were identified. The most common community grouping \nconsisted of Physcia millegrana Degel, Candelaria concolor (Dicks) B. Stein, \nPhyscia aipolia (Ehrh ex Humb.) Furnrohr; all of which are known nitrophytes. The \nrelationship between substrate pH and lichen species richness was examined. Sites \nwith a known source of anthropogenic chemical contamination were found to have a \ncorrelation of l=0.8 between lichen species richness and pH. The inverse was found \nfor sites with no known source of contamination with a correlation of r \n2 \n=-0.72. The \nfindings suggest that species richness may be influenced by altering substrate pH \nwhich promotes the growth of nitrophytic species capable of tolerating high S02 \nlevels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it