Expanding and testing fluorescent amplified fragment length polymorphisms for identifying roots of boreal forest plant species
Bibliographic record
Abstract
Premise of the Study Identifying roots to species is challenging, but is a common problem in ecology. Fluorescent amplified fragment length polymorphisms (FAFLPs) can distinguish species within a mixed sample, are high throughput, and are inexpensive. To broaden the use of this tool across ecosystems, unique size profiles must be established for species, and its limits identified. Methods Fragments of three noncoding cpDNA regions were used to create size profiles for 193 species common to the western Canadian boreal forest. We compared detection success among congeners using FAFLPs and Sanger sequencing of the trnL intron. We also simulated and experimentally created communities to test the influence of species richness, cpDNA regions used, and extraction/amplification biases on detection success. Results Of the 193 species, 54% had unique size profiles. This value decreased when fewer cpDNA regions were used. In simulated communities, ambiguous species identifications were positively related to the species richness of the community. In mock communities, some species evaded detection owing to poor extraction or amplification. Sequencing did not increase detection success compared to FAFLPs for a subset of 24 species across nine genera. Discussion We recommend FAFLPs are best suited to confirm rather than discover species occurring belowground.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".