A molecular approach to quantify root community composition in a northern hardwood forest — testing effects of root species, relative abundance, and diameter
Bibliographic record
Abstract
Research on root community structure is currently limited by the methodologies available. We evaluated a molecular-sequence-based approach to quantify the relative abundance of roots from different plant species in mixed samples. We extracted DNA from mixtures of roots, amplified the trnL intron by polymerase change reaction, and identified up to 60 clones from each mixture. We tested the effects of root diameter and species on sequence representation in mixtures. Species were correctly identified in our mixtures. Recovery efficiencies were low for root diameter classes >0.3 mm compared with those <0.3 mm, and species in high abundance in the mixture had relatively low recovery efficiency. American beech ( Fagus grandifolia Ehrh.) was quantitatively underrepresented compared with yellow birch ( Betula alleghaniensis Britton) and white ash ( Fraxinus americana L.). Root identification by sequencing is accurate and can readily be applied to novel systems without new primer development. This technique will be attractive for documenting changes in relative abundances of species, especially as the cost of sequence-based analyses drops. However, the results of such analyses must be considered carefully, as root diameter distribution, abundances, and species can introduce quantifiable biases in the estimation of relative root abundance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".