Bibliographic record
Abstract
This study is dedicated to the critical task of predicting mushroom toxicity and thoroughly analyzing its underlying toxicological attributes. Employing an innovative approach, the research unveils a decision tree classifier that exhibits an impressive accuracy of 0.985, a testament to its exceptional ability in distinguishing between toxic and edible mushrooms. Crucial factors such as appearance, odor, and cap texture emerge as key determinants in achieving this remarkable outcome. The model's exceptional precision and recall rates endow it with the potential to serve as an invaluable tool for diverse stakeholders, including foragers, consumers, and regulatory authorities, thereby significantly elevating the overall landscape of food safety measures. Furthermore, the study strategically outlines promising directions for future research endeavors, accentuating the significance of refining model performance and extending its applicability ambit to encompass a broader array of plant species. By significantly advancing the realm of predictive modeling within the intricate context of mushroom toxicity, this research substantially augments the arena of public health and safety. It further underscores the pivotal role of cutting-edge data-driven methodologies in effectively upholding human well-being and ecological equilibrium within the intricacies of our interconnected global milieu.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".