No. 0 (16s): Raw 16S rRNA fastq data files for Bocas hypoxia study
Bibliographic record
Abstract
This repository contains the RAW sequencing data for hypoxia study. Trimmed reads (with primers removed) are deposited at the European Nucleotide Archive, study accession number PRJEB36632 (ERP119845). Raw fastq data files are named using the root format RunQ_XYZ, where Q is the run number (1, or 2), X is the habitat type (Coral, Water, Sediment, Mat), YZ is the site (CC = Coral Cay; CR = Cayo Roldan), and in some cases a replicate number. So...Run01_CCR1_R2_001.fastq...corresponds to the reverse reads (R2) of a Cayo Roldan (CR) coral sample (C), replicate #1, Run 1. Raw fastq files are deposited here by Run.We used the PowerSoil® DNA Isolation Kit (MoBio) following the manufacturer's protocol to extract community DNA from each sample. Extracted DNA was sequenced on an Illumina MiSeq by Integrated Microbiome Resource at the Centre for Comparative Genomics and Evolutionary Bioinformatics (Dalhousie University). We targeted the V4-V5 hypervariable region using 515F (5′-GTGYCAGCMGCCGCGGTA) and 926R (5′-CCGYCAATTYMTTTRAGT). We collected 38 samples encompassing two sites and four habitat typesWe generated sequence data for all 38 samples. In the first run (Run01), all samples were sequenced and, due to lower than average yield, several were re-sequenced (Run02). This repo also contains the metadata files for submitting the trimmed data to the European Nucleotide Archive (ENA). Please do confuse this datasets with the dataset archived on the ENA (accession number PRJEB36632). In order to submit to the ENA, we had to first remove primers from the raw data and then upload the data along with these metadata files. The first publication concerns the water samples only. If you are interested in those samples only they are also available in the WATER_ONLY.zip file.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.207 | 0.114 |
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".