Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We downloaded genomic and transcriptomic read data used in this study from the NCBI database (Table 2 ). The genome and transcriptome reads were de-duplicated to remove polymerase chain reaction products using Dedupe in BBTools ( https://sourceforge.net/projects/bbmap/ ). Subsequently, the reads were quality controlled using TrimGalore ( https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ) by filtering out the reads with bad base quality (<20) and short length (<40). We aligned filtered genomic and transcriptome reads against the corresponding genome assemblies using Bowtie v2.3.5.1 ( http://bowtie-bio.sourceforge.net ) and HISAT v2.1.0 ( http://www.ccb.jhu.edu/software/hisat/ ), respectively. The candidate RNA-editing sites were detected using JACUSA v1.3.0 [ 9 ] (call-2 --filter-flags 1024 --min-mapq1 0 --min-mapq2 0 --pileup-filter S). The variants supported with <20 total mapped reads, <5 or <10% of variant reads, and any sites with matching gDNA variants (>5% variants of mapped reads) were ignored. We used SPAdes v3.13.0 ( http://cab.spbu.ru/software/spades ) (--careful --cov-cutoff auto) to reassemble D. quercina genomic reads. The original and newly assembled genomes were aligned using NUCmer 4.0.0beta2 ( http://mummer.sourceforge.net ) to find corresponding sites. The RNA reads from the same isolates that DNA reads were generated were downloaded from MycoCosm ( https://mycocosm.jgi.doe.gov/ ) [ 10 ].
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.
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 it