Valuing native ectomycorrhizal fungi as a Mediterranean forestry component for sustainable and innovative solutions
Bibliographic record
Abstract
Native ectomycorrhizal fungi (ECMF) represent an emergent critical support in forestry and bioindustry while providing an attractive economical return and ecosystems services. These attributes are desirable given the human activities that are affecting ecosystems and biodiversity worldwide. The Mediterranean region has inherited native forests that are in serious decay, with serious environmental and socioeconomic consequences as a result of human influence in shaping ecosystems, particularly over the last century. In this context, edible ECMF are important not only because of their value for ecosystem functions, but also for their organoleptic and nutritive properties, and because of the presence of bioactive compounds. In this paper, we discuss critical aspects of ECMF diversity and traits for forest health, productivity, and sustainability, as well as the importance of exploring biologically active proteins obtained from native ECMF as sources for future forest management planning and industry innovation. The use of convergent approaches to ameliorate the identity of ECMF reservoirs in forest ecosystems and rural lands is urgently required to restore and protect native biodiversity and ecosystems services and meet efficient production solutions to provide sustainable innovation while ensuring environmental safety.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".