Comparing the relative effects of abiotic and biotic drivers on the host mycorrhizal types of canopy trees
Bibliographic record
Abstract
Recently, research in a variety of forest ecosystems has shown that trees of the same species (conspecifics) may experience directional plant-soil feedbacks (PSFs) depending on whether they engage in arbuscular mycorrhizal (AM) symbioses or ectomycorrhizal (EcM) symbioses. Some of the same processes hypothesized to be involved in conspecific feedbacks could also produce feedbacks between heterospecific species. For example, common mycorrhizal networks could conceivably connect plants of different species, and changes in the local nutrient economy can favour other species with the same host mycorrhizal type (AM or EcM). An earlier study we completed found a positive, albeit weak, correlation between the amount of cover of AM hosts in the canopy and the amount of cover of AM hosts in the ground layer, and likewise for EcM hosts (Kudla et al., in review). However, AM and EcM plant hosts tend to have different abiotic niches (Steidinger et al., 2019; Barceló et al., 2019), and in this study we will quantify the effects of abiotic drivers (temperature, precipitation, soil chemistry) and biotic drivers (the proportion of like mycorrhizal type in the canopy layer) on the relative cover of AM and EcM hosts in the ground layer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".