A PCR-based method for the identification of the roots of 10 co-occurring grassland species in mesocosm experiments
Bibliographic record
Abstract
An understanding of factors influencing the distribution of plant roots is intimately linked to our understanding of basic ecosystem functions such as nutrient flux and productivity. However, it is not usually possible to measure root distributions because it is difficult to identify the roots of different species when they are grown in mixture. This is because the roots of most species are not visually distinguishable. We designed a simple, PCR-based method for the identification of roots in mesocosm experiments, which we have applied to 10 co-occurring grassland species. Species-specific primers based on ITS sequences from GenBank were evaluated in PCR assays using either homogeneous or heterogeneous DNA templates, as well as DNA extracted from mixed-root samples from multiple combinations of species. The species-specific primers reported here produced accurate identifications, free from both false negatives and false positives, in 100% of our assays. We also evaluated the sensitivity of our system and demonstrated detection of species when they comprised as little as 0.05 ng of target DNA mixed in a total of 2.5 ng of multi-species template DNA. Our PCR-based method for root identification in mesocosms is more cost effective, and simpler to apply than previously described methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".