Investigating Dispersion of Leaf Symbionts: Exploring Specific Symbioses
Bibliographic record
Abstract
Symbioses can range from mutualisms to parasitisms; the latter are the foci of this exercise. The way in which parasites are distributed among hosts (their dispersion) can have profound importance for how they and their hosts coevolve, and for many other facets of their biology. Accordingly, many researchers, including ecologists and medical practitioners, study dispersion of parasites in detail. Fungi are commonly observed parasites on leaves of trees. I describe one way to randomly sample leaves to quantify dispersion of such parasites and test whether dispersion is related to a variety of explanatory variables. Significant quantities of data can be generated in relatively short order and pooled for a class; many patterns can emerge that challenge students to find logical interpretations. Relatively sophisticated students could test whether parasites have a random dispersion pattern by comparing the histogram they generate to that of a Poisson distribution. Data can be analyzed in a simple fashion or via advanced mixed models.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".