Fostering comprehension and integration in mycorrhiza biology: conceptual scaffolding as an aid in teaching and exploration<sup>,</sup>
Bibliographic record
Abstract
Over the last decade, we have witnessed extraordinary progress in the understanding of molecular dialogues between the partners in plant root mutualisms and, as such, a considerable amount of new information now needs to be integrated into an already significant body of literature. The topic of symbiosis has become difficult to explore in a teaching venue, as there is seemingly so much to discuss, and yet students are truly interested in the discipline because of its potential applications in conservation, sustainable agriculture, and forestry. In this minireview targeted to instructors, senior students, and scientists, we offer a means of teaching the symbioses between mycorrhizal fungi and vascular plants, whereby we propose a conceptual staircase with three levels of incremental learning difficulty. At the first level, we describe the fundamentals of mycorrhizas with special emphasis on the plant–fungus interface. At the second level, we focus on the pre-communication between the two partners. At the third level, we discuss the physiology of the interface in terms of agriculture and forestry. At the end of each level, we provide a short summary where the most important concepts have been outlined for an instructor. As well, throughout the text, we raise questions of interest to the field at large.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".