Endofüütsed bakterid Eestis kasvatatavates juurviljades
Bibliographic record
Abstract
Antud töös uuriti viie Eestis kasvatatava juurvilja koore ja sisu bakterite kooslusi ning optimeeriti katsesüsteemi, eesmärgiga saavutada esinduslik valim juurviljade endofüütsete bakterite liigirikkusest. Selleks kasutati kahte erinevat DNA eraldamismeetodit ja bakterite identifitseerimiseks amplifitseeriti PCR 16S rDNA V4 hüpervariaabel regiooni, mis sekveneeriti Illumina MiSeq tehnoloogiaga. Kahe erineva DNA eraldamismeetodiga õnnestus samadest proovidest identifitseerida olulisel määral erinevad bakteriliigid. Analüüs näitas, et juurviljade koore endofüütsete bakterite kooslused on mitmekesisemad, kui sisu omad. Kõikide juurviljade koores kui ka sisus domineerisid Gammaproteobacteria esindajad. Koores oli rohkem Alpahaproteobacteria, Actinobacteria, Betaproteobacteria ja Bacteroidetese esindajaid. Sisus oli lisaks gammaproteobakteritele ka klass Bacilli esindajaid. Tööst selgus, et juurviljade mirkobioom on väga mitmekesine ja oluline on jätkata nende edasist uurimist.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".