Exploring XmoA gene profiles in Saanich Inlet with TreeSAPP
Bibliographic record
Abstract
This is a package of FASTQ and FASTA files containing subsetted data from Saanich Inlet Cruise 72. These data are intended for the purpose of demonstrating the utility of TreeSAPP for gene-centric metagenome interpretation. This dataset is integrated with UBC's Microbiology and Immunology class, MICB425. A bookdown for the tutorial is available on GitHub: https://github.com/EDUCE-UBC/MICB425 Sequences were subset according to their relationship to the XmoA (CuMMO protein family) gene. Content: 6.9M SI072_100m_MetaT_QC_Filtered.fq.gz 741K SI072_100m_pe.1.fq.gz 739K SI072_100m_pe.2.fq.gz 31K SI072_10m_MetaT_QC_Filtered.fq.gz 12K SI072_10m_pe.1.fq.gz 13K SI072_10m_pe.2.fq.gz 6.4M SI072_120m_MetaT_QC_Filtered.fq.gz 563K SI072_120m_pe.1.fq.gz 561K SI072_120m_pe.2.fq.gz 6.3M SI072_135m_MetaT_QC_Filtered.fq.gz 638K SI072_135m_pe.1.fq.gz 637K SI072_135m_pe.2.fq.gz 6.1M SI072_150m_MetaT_QC_Filtered.fq.gz 538K SI072_150m_pe.1.fq.gz 535K SI072_150m_pe.2.fq.gz 1.4M SI072_165m_MetaT_QC_Filtered.fq.gz 951K SI072_165m_pe.1.fq.gz 947K SI072_165m_pe.2.fq.gz 114K SI072_200m_MetaT_QC_Filtered.fq.gz 75K SI072_200m_pe.1.fq.gz 75K SI072_200m_pe.2.fq.gz 18M SI072_MAGs.fa 8.2K SI072_MAGs_gtdbtk.bac120.summary.tsv 85K SI072_MetaG_contigs.fasta
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.024 |
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".