THEY HOLD HANDS! A MUSHROOM MAP OF MAINE
Bibliographic record
Abstract
Mapping is a traditional technique of representation (Radović, 2016) that can be used to visualize where specific components of interest are located. Maps of organisms are commonly used to predict their distribution and their relationships to various components of nature, such as aspects of the environment. Trees are often easily mapped as they are immobile and easily identifiable. Mushrooms, or macrofungi, however are more difficult to map because they are cryptic and ephemeral. But, mushrooms often have strong associations with trees. Thus, by using forest composition mapping tools, and forest inventory data, a map predicting where mushroom species are located can be made by using the macrofungal tree associations. I used such an approach to build mushroom maps for Maine. A mushroom map provides a novel approach of visualizing and estimating the locations of mushroom species by utilizing their symbiotic relationships with trees. Such maps are valuable as they can provide new insight to mycologists, mycophagists, foragers, foresters, silviculturists, biologists, and other key groups searching for more expansive information regarding mushroom locations. The following 20 maps were generated utilizing forest inventory analysis (FIA) plots, ArcGIS, and Mushrooms of the Northeast United States and Eastern Canada by Timothy Baroni.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".