A molecular identification protocol for roots of boreal forest tree species
Bibliographic record
Abstract
PREMISE OF THE STUDY: Roots play a key role in many ecological processes, yet our ability to identify species from bulk root samples is limited. Molecular tools may be used to identify species from root samples, but they have not yet been developed for most systems. Here we present a PCR-based method previously used to identify roots of grassland species, modified for use in boreal forests. • METHODS: We used repeatable interspecific size differences in fluorescent amplified fragment length polymorphisms of three noncoding chloroplast DNA regions to identify seven woody species common to boreal forests in Alberta, Canada. • RESULTS: Abies balsamea, Alnus crispa, Betula papyrifera, Pinus contorta, and Populus tremuloides were identifiable to species, while Picea glauca and Picea mariana were identifiable to genus. In mixtures of known composition of foliar DNA, species were identified with 98% accuracy using one region. Mixed root samples of unknown composition were identified with 100% accuracy; four species were identified using one region, while three species were identified using two regions. • DISCUSSION: This methodology is accurate, efficient, and inexpensive, and thus a valuable approach for ecological studies of roots. Furthermore, this method has now been validated for both grassland and boreal forest systems, and thus may also have applications in any plant community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".