Lichen Diversity in Portland Metro Area
Bibliographic record
Abstract
Lichens are keystone species ubiquitous around the world. Lichens are important air quality indicators across urban and natural ecosystems. The biodiversity of lichens can be great, but as species sensitive to air-pollution, the biodiversity in urban ecosystems does not always match the biodiversity of surrounding natural areas. For this work I used the citizen science tool iNaturalist to compile a list of the most frequently observed lichen species in the Portland-Vancouver Metro Region. Public tools like iNaturalist make lichen surveys accessible to more people. I present details about how to identify the five most frequently observed lichen species, and summary statistics about the twenty most observed species which span 11 fungal families. Parmeliaceae is by far the most common family representing 7 of the top 20 observed lichen species (35%), with Hypogymnia representing the genus of the most species (3 of 20). The most frequent type of thallus (lichen body) is foliose, representing 60% of the top observed species. I will give a brief overview of lichen biology and their ecology. Becoming familiar with lichens is the first step to promoting the conservation of these important organisms. The persistent nature of lichens enables the study of these organisms in any season. I aim to show that anyone can be a lichenologist.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 0.001 |
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".