The community of root fungi is associated with the growth rate of Norway spruce ( <i>Picea abies</i> )
Bibliographic record
Abstract
Our study delved into the relationship between root-associated fungi, gene expression and plant morphology in Norway spruce cuttings derived from both slow-and fast-growing trees. We found no clear link between the gene expression patterns of adventitious roots and the growth phenotype, suggesting no fundamental differences in the receptiveness to fungal symbionts between the phenotypes. Interestingly, saplings from slow-growing parental trees exhibited a higher richness of ectomycorrhizal species and larger roots. Some ectomycorrhizal species, typically found on mature spruces, were more prevalent on saplings from slow-growing spruces. The ericoid mycorrhizal fungus, Hyaloscypha hepaticola, showed a stronger association with saplings from fast-growing spruces. Moreover, saplings from slow-growing spruces had a greater number of Ascomycete taxa and free-living saprotrophic fungi. Aboveground sapling stems displayed some phenotypic variation; saplings from fast-growing phenotypes had longer branches but fewer whorls in their stems compared to those from the slow-growing group. In conclusion, the observed root-associated fungi and phenotypic characteristics in young Norway spruces may play a role in their long-term growth rate. This suggests that the early interactions between spruces and fungi could potentially influence their growth trajectory.
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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) | 0.000 | 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".