Additional file 1 of Large-scale discovery of male reproductive tract-specific genes through analysis of RNA-seq datasets
Bibliographic record
Abstract
Additional file 1: Tables S1, S2, S11, S12, S13, and S14. Table S1. Summary of Human RNA-seq datasets. This table contains the SRA value for each previously published human RNA-seq dataset that was reanalyzed as part of this study. The GEO accession number for each new human RNA-seq dataset generated and subsequently analyzed in this study is also included. Table S2. Summary of Mouse RNA-seq datasets. This table contains the SRA value for each previously published mouse RNA-seq dataset that was reanalyzed as part of this study. The GEO accession number for each new mouse sample generated and subsequently analyzed in this study is also included. Table S11. Single-guide RNAs targeting the genes’ upstream (U) and downstream (D) regions used for generating knockout mice. Efficiency of embryo transplantation was presented using the number of total pups delivered by pseudopregnant mice divided by the number of total embryos used for oviduct transplantation (Total pups/embryos transplanted). Efficiency of genome editing was determined by the number of pups carrying enzymatic mutations divided by the number of pups subjected to genotyping (GM pups/pups genotyped). Table S12. Sanger sequencing of detailed genotype of mutant DNA sequences in all the five mouse lines. Table S13. Primers and PCR conditions used for genotyping the mutant alleles of the knockout mouse lines. Table S14. Human and mouse RT-PCR primer sequences used for verification of reproductive tract-specificity.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.612 | 0.111 |
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".