Predictors of growth of ectomycorrhizal fungal mycelium vary with host ( <i>Pinus strobus</i> ) phenology
Bibliographic record
Abstract
Ectomycorrhizal fungi (EcMF) are major contributors to belowground ecosystem production, yet our understanding of seasonal regulation of growth in EcMF is sparse. Both abiotic and biotic factors are likely to influence EcMF growth. We hypothesized that (1) soil temperature would predict mycelial growth, (2) soil moisture would be a good additional predictor of growth, and (3) host plant leaf expansion would reduce mycelial growth rates compared with non-expansion periods. At the Houghton Rhizotron we monitored EcMF growth on white pine roots at bi-weekly intervals for 2 years, using GIS-based change analysis to quantify mycelial growth. Growth was predicted by temperature over the entire year, including wintertime, when hyphae grew slowly through near-freezing soils. During leaf expansion period, temperature explained less variance in growth rates, and the relationship had a lower slope. Soil moisture added little predictive power. This information can inform belowground carbon cycling models in pine forests.
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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 | 0.001 |
| 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 | 0.000 | 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".