Does a fungal species drive ectomycorrhizal root traits in Alnus spp.?
Bibliographic record
Abstract
Ectomycorrhizal (EcM) fungi contribute significantly to the shaping of short-root morphology, playing an important role in balancing the costs and benefits of root growth and nutrient uptake and exchange in boreal forests. We aimed to assess the effect of various EcM fungal taxa on root traits at seven sites dominated by grey alder, Alnus incana (L.) Moench, and black alder, Alnus glutinosa (L.) Gaertn. Mean root size, specific root length, specific root area, root tissue density, and root-tip frequency of EcM short roots were measured in EcM anatomotypes in relation to the effects of host species, soil moisture level, and nutrient status. Redundancy analysis revealed that anatomotype, alder species, site, and soil parameters (N, P, K, Ca, and Mg concentrations, pH, organic-matter content) accounted for 42.3% (p < 0.001) of the total variation in EcM root morphology. Variation decreased in the following order: anatomotypes (27.9%) > soil parameters and sites (19.9%) > alder species (5.1%). EcM fungus species had the primary influence on EcM short-root size. EcM roots of the dominant anatomotype, Alnicola spp., had the highest specific root length and specific root area in both alder species. Short-root morphology depends most strongly on the fungal taxa involved, which indicates that the type of mycobiont has an important influence on the functional properties of fine roots.
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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 | 0.000 | 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) | 0.001 | 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".