RNA Isolation from Plant Tissue Protocol 2: McKenzie et al’s Qiagen hybrid method v1
Bibliographic record
Abstract
Implemented by: Jim Leebens-Mack and Charlotte Carrigan This protocol was developed by McKenzie et al.2 to facilitate the isolation of RNA from woody plants rich in phenolics and polysaccharides, such as grapes (Vitaceae) and fruit bearing Rosaceae (apples, cherries and pears). The protocol is provided on Qiagen’s website as an alternative method to be used in combination with their RNeasy Plant Minikit. We repeat the protocol here in the event that this protocol is removed from Qiagen’s website or readers find it difficult to obtain the original publication. This protocol is part of a collection of eighteen protocols used to isolate total RNA from plant tissue. (RNA Isolation from Plant Tissue Collection: https://www.protocols.io/view/rna-isolation-from-plant-tissue-439gyr6) 2 McKenzie, D.J., McLean, M.A., Mukerji, S. & Green, M. Improved RNA extraction from woody plants for the detection of viral pathogens by reverse transcription‐polymerase chain reaction. Plant Disease 81, 222‐226 (1997).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.082 | 0.061 |
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".