Bibliographic record
Abstract
With the current rate of biodiversity loss in plants, it is essential to develop and combine in situ and ex situ methods for an integrated approach to plant conservation. Central to integrated conservation approaches is the necessity for assessing genetic diversity within the threatened population. Among the tools available to assess genetic diversity is microsatellite analysis. Microsatellites are variable number tandem repeats, or short repetitive sequences, that are useful due to their abundance within the genome, high mutation rate, and high levels of polymorphism. This project aimed to develop microsatellite markers for the vulnerable orchid, Cypripedium passerinum, for the purpose of assessing genetic diversity in populations within the Wagner Natural Area, Alberta, Canada. Fast Isolation by AFLP of Sequences Containing Repeats (FIASCO) was used to generate an (AC)n microsatellite enriched library from DNA samples of C. passerinum. A total of 84 clones from this library were isolated for sequence analysis to identify inherent microsatellite sequences. Primers designed to amplify the identified microsatellite sequences will be useful tools in assessing the genetic diversity of C. passerinum populations and can be applied to closely related species such as C. pubescens. Such genetic diversity assessment will inform conservation efforts of this threatened terrestrial orchid species. Department: Biology Faculty Mentor: Dr. David McFadyen
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".