Assessing Belowground Plant Diversity in Wetland Soil through DNA Metabarcoding: Impact of DNA Marker Selection and Analysis of Temporal Patterns
Bibliographic record
Abstract
This thesis is an investigation of the DNA metabarcoding approach to biodiversity assessment of vascular plant diversity. Specifically, the investigation focused on DNA metabarcoding of environmental DNA extracted from unsorted soil samples. There were two main research goals: to evaluate the suitability of four established DNA marker regions – matK, rbcL, ITS2, and the P6 loop of the trnL intron – for biodiversity assessment of vascular plants and to examine community turnover in total belowground vascular plant diversity. Based on the relative annotation, resolution and recovery ability of the DNA markers, rbcL and ITS2 were recommended for future biodiversity assessments. Annual variability in belowground diversity was consistent in magnitude with previous aboveground observations suggesting that accumulation of plant tissues is not a major restriction for soil-based biodiversity assessments. Finally, an interaction between DNA marker and observed community turnover was identified and positively correlated with length of DNA marker.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".