Données d'analyse pour : "Microbial community transfers across a pilot ripening cellar are increased by cheese wiping"
Bibliographic record
Abstract
Quarto scripts that have be used for the publication Microbial community transfers across a pilot ripening cellar are increased by cheese wiping. The folder zip archive contains 4 folder: Metabarcoding analyses (16S) = `MetaB16S` Metabarcoding analyses (ITS) = `MetaBITSS` Analyses from culture-depend data and from gases data (CO2) = `Microbiology_gases` All data = `Raw_data` Metab16S In the MetaB16S folder ==\> 2 documents, the quarto file and the html version, related with analyses of bacterial composition and diversity from all environments (cheese, cloth, air and wall's cellars). The quarto file contains the following analyses. Cheese microbial characteristics Barpolt of composition of short-ripened cheese and long-ripened cheese (separeted by time and cellars) Alpha diversity was measured with the Shannon and Richness observed indexes by ripening stage, time and cellar Beta diversity analyses were generated by ripening stage, time and cellar (+ Permanova analyses and pairwise adonis when necessary) All env microbial characteristics Same analyses as for cheese microbial characteristics were performed to describe microbial evolution for all environments (cheese, cloths, air and wall'cellars) Flux - all environments Microbial transfers between all environments ==\> heatmap from ASVs at least shared with two environments and gather at species level and then at order level. In this heatmap, we can see the prevalence (Blue = weak, red = strong) of thoses ASVs at different relative abundance (absciss axe) for 6 environments (long-ripened cheese, short-ripened cheese, long-ripened cheese cloth, short-ripened cheese cloth, air and wall). This heatmap was generated for the three cellar separately. MetaBITS In the MetaBITS files there are two documents (quarto file with the script and the html version) related with bacterial composition of all environments (cheese, cloth, air and wall's cellars) In the quarto file, the phyloseq files was loaded to generate the following figures. Same analyses as MetaB16S were generated for fungal community. Differential analyses Differential analyses performed on cheese samples ==\> (short-ripened cheeses stored with vs without long-ripened cheeses) Microbiology_gases In this this barplot of levels of molds in the air according wiping activity ==\> comparison of 2 media (OGA vs MEA) A figure of the evolution of CO2 during one day in one cave was generated. On this figure we can see the concentration of CO2 increasing when cheeses were being wiped and its diminution. Then there is zoom on some specific period when the CO2 increased when cheese were not being wiped. Raw_data `2023-06-07_synthese_microbio_SN_1204`file (.xlsx) , sheet = Microbiologie ==\> data related with enumeration for each environment + metadata (date of sampling, cellar, wiping conditions...) `2024-10-10_Phyloseq_SN_16S` file (.Rdata) ==\> phyloseq file use to performed analyses MetaB16S `2024-10-10_Phyloseq_SN_ITS` file (.Rdata) ==\> phyloseq file use to performed analyses MetaBITS `2024-10-14_Synthese_gases_all_cellars1` file (.xlsx)==\> several sheets: `Evolution of CO2C2, Cave 3 and Cave 4` = Evolution of CO2, relative humidity, temperature and atmospheric pressure from 12/04/2023 to 17/05/2023. `CO2_suivi_C2, CO2_suivi_C3 and CO2_suivi_C4` = For each cellar (C2,C3 and C4) ==\> focus evolution on of CO2, relative humidity, temperature and atmospheric pressure on 1 day `NH3` = Evolution of NH3 from 12/04/2023 to 12/05/2023 [EBI BioProject](https://www.ebi.ac.uk/ena/browser/view/PRJEB91282)
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.376 | 0.252 |
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".