Fungal Endophytes of <i>Alnus incana</i> ssp. <i>rugosa</i> and <i>Alnus alnobetula</i> ssp. <i>crispa</i> and Their Potential to Tolerate Heavy Metals and to Promote Plant Growth
Bibliographic record
Abstract
Soil contamination by metals is of particular interest, given that their retention times within the profile can be indefinite. Thus, phytostabilization can be viewed as a means of limiting metal toxicity in soils. Due to their ability to grow on contaminated soils, alders have repeatedly been used as key species in phytostabilization efforts. Alder ability to grow on contaminated sites stems, in part, from its association with microbial endophytes. This work emphasizes the fungal endophytes populations associated with Alnus incana ssp. rugosa and Alnus alnobetula ssp. crispa (previously A. viridis ssp. crispa) under a phytostabilization angle. Fungal endophytes were isolated from alder trees that were growing on or near disturbed environments; their tolerances to Cu, Ni, Zn, and As, and acidic pH (4.3, 3, and 2) were subsequently assessed. Cryptosporiopsis spp. and Rhizoscyphus spp. were identified as fungal endophytes of Alnus for the first time. When used as inoculants for alder, some isolates promoted plant growth, while others apparently presented antagonistic relationships with the host plant. This study reports the first step in finding the right fungal endophytic partners for two species of alder used in phytostabilization of metal-contaminated mining sites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".