<b>Metadata for the article "</b><b>Interplay of biotic and abiotic factors shapes tree seedling growth and root-associated microbial communities"</b>
Bibliographic record
Abstract
Root-associated microbial communities can alleviate plant abiotic stresses, thus potentially supporting adaptation to a changing climate during range expansion. While climate change is extending plant species fundamental niches northward, the distribution and colonization of mutualists (e.g., arbuscular mycorrhizal fungi) and pathogens may constrain plant growth and regeneration. Yet, the degree to which biotic and abiotic factors impact plant performance and associated microbial communities at the edge of their distribution remains unclear. In this study, we used a combination of root microscopy and amplicon sequencing to characterize tree root microbial communities (soil and root mycorrhizae, bacteria, and fungi) of sugar maple seedlings. We also explored the affect of abiotic and biotic factors on root microbial communities and their relationship with seedling growth along two elevation gradients in Quebec, Canada.<br>Metadata:comm_16S_2023: Bacteria ASV table with sequence counts per sample comm_ITS_2023: Fungi ASV table with sequence counts per sample comm_AMF_2023: Arbuscular mycorrhiza fungi (AMF) ASV table with sequence counts per sample taxa_16s_2023: Bacteria taxonomy taxa_ITS_2023: Fungi taxonomytaxa_AMF_2023: AMF taxonomy fungi_guild_meg: Table containing functional assignments for fungi described from one of the elevation gradientsfungi_guild_sut: Table containing functional assignments for fungi described from one of the elevation gradientsMetadata_clean_2023: Table containing information on the variables used in the modelling. Contains numerical data behind Figure 2 in the article, such as seedling annual growth (ann_growth) and elevation (alt).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".