Fungi and diseases — natural components of healthy forestsThis article is one of a collection of papers based on a presentation from the <i>Stem and Shoot Fungal Pathogens and Parasitic Plants: the Values of Biological Diversity</i> session of the XXII International Union of Forestry Research Organization World Congress meeting held in Brisbane, Queensland, Australia, in 2005.
Bibliographic record
Abstract
Forest health is described and perceived in different ways by the general public, land owners, managers, politicians, and scientists, depending on their values and objectives. Native tree pathogens and diseases are often associated with negative impacts even though damage is limited or not widespread. Too often, the concepts of tree health and forest health are used interchangeably and are not related to scale. Similar to fire, occurrences of disease outbreaks focus on the negative effects. However, native pathogens often exist in equilibrium with natural forest communities so their critical ecological roles are not easily discernible. Examined holistically, native fungi and diseases, dead and dying trees, and the many complex ecological interactions among them provide valuable benefits that ultimately contribute to sustainable, healthy forest ecosystems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".