An amplicon panel for high-throughput and low-cost genotyping of Pacific oyster
Bibliographic record
Abstract
Data inputs for the manuscript "An amplicon panel for high-throughput and low-cost genotyping of Pacific oyster Crassostrea gigas". Includes:Data from Sutherland et al. 2020, not originally uploaded and uploaded with permission from primary author:- populations_single_snp_HWE.raw (plink format, single-SNP per locus filtered output from Stacks populations module)- populations.snps.vcf (as above, but VCF format)New data, multilocus genotypes from the pilot study from the present analysis:- R_2022_08_04_S5XL.xls (initial run samples using Cgig_v.1.0 amplicon panel)- R_2022_10_07_S5XL.xls (second run using Cgig_v.1.0 amplicon panel)- my_data_ind-to-pop_annot (tab-delim text file interp of populations; note: samples 1601-1608 are cultured samples from China, not wild samples, and this is dealt with in the analysis script)- selected_mnames.csv (tab-delim text file that outlines each marker name and the reason it was included in the design)- GBMF_pilot_sample_ID_barcode_run_from_variantCaller_output_2023-07-27.xlsx (Excel file showing run name, barcode ID, and sample ID for the pilot study) - rubias_142_ind_364_loc_2024-04-03.txt (rubias file, all filtered loci for parentage analysis) - rubias_142_ind_328_loc_2024-04-03.txt (rubias file, all filtered loci, and any loci with significant Mendelian incompatibilities removed, please see published article) New data, multilocus genotypes from the Oregon State University (OSU) Molluscan Broodstock Program (MBP):- R_2023_07_26_12_44_23_user_GSS5PR-0268-78-Ampseq_Oyster_20230725.xls (OSU CHR8 samples using Cgig_v.1.0 amplicon panel)- my_data_ind-to-pop_annot_OSU_MBP.txt (population map, will need to rename as my_data_ind-to-pop_annot.txt to match requirement in scripts).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.085 |
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".