COMPUTATIONAL META-ANALYSIS OF CERVICAL CANCER USING AVAILABLE 16S RRNA NGS DATA
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
Cervical cancer is one of the most frequently occurring and deadliest gynaecological cancer which develops in cervical cells. Since it develops in tissues lining the internal organs it is a Carcinoma. Human papilloma virus infection is found to top the list of carcinogenic factors. Overexpression of certain proteins due to HPV integration in host body over a time can result in carcinoma. However vaginal microbiota plays a key role in development, persistence and progression of infections leading to diseases such as cervical cancer. Some recent studies have revealed potential roles of microbiome in cervicovaginal diseases. Thus a comparative metagenomic study among such samples can uncover microbial diversities present in these samples. Due to presence of highly conserved regions as well as hyper variable regions 16s rRNA gene sequence is selected for identification and classification of bacterial diversity. For the purpose of metagenomic analysis 16s rRNA gene sequences were analysed using QIIME pipeline.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.022 | 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