MétaCan
Menu
← Back to cohort
Record W4394444866 · doi:10.6084/m9.figshare.20762889

Additional file 2 of Lactic acid from vaginal microbiota enhances cervicovaginal epithelial barrier integrity by promoting tight junction protein expression

2022· dataset· en· W4394444866 on OpenAlexaff
David Jose Delgado-Diaz, Brianna Jesaveluk, Joshua A. Hayward, David Tyssen, Arghavan Alisoltani, Matthys Potgieter, Liam Bell, Elizabeth Ross, Arash Iranzadeh, Imane Allali, Smritee Dabee, Shaun Barnabas, Hoyam Gamieldien, Jonathan M. Blackburn, Nicola Mulder, Steven B. Smith, Vonetta L. Edwards, Adam Burgener, Linda‐Gail Bekker, Jacques Ravel, Jo‐Ann S. Passmore, Lindi Masson, Anna C. Hearps, Gilda Tachedjian

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTight junctionLactic acidMicrobiologyChemistryCell biologyBiologyBacteriaGenetics

Abstract

fetched live from OpenAlex

Additional file 2: Supplementary Table 1. Proteins with significantly different abundance in women with high as compared to low L-LDH and D-LDH abundance*. Supplementary Table 2. Barrier-related proteins with significantly different abundance in women with high as compared to low L-LDH abundance*. Supplementary Table 3. Genes differentially expressed by L-LA, D-LA and HCL treatment of Ect cells*. Supplementary Table 4. Gene ontology pathways significantly enriched by L-LA and D-LA treatment of Ect cells. Supplementary Table 5. Tight junction genes differentially expressed by L-LA, D-LA and HCL treatment of Ect cells*. Supplementary Table 6. Tight junction genes differentially expressed by treatment of VK2 cells with bacterial culture supernatants*.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.374
Threshold uncertainty score0.893

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.3740.090

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.

Opus teacher head0.016
GPT teacher head0.276
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOpen MIND→Same topicPelvic floor disorders treatments→French-language works237,207→