Optimised DNA isolation from marine sponges for natural sampler DNA (nsDNA) metabarcoding
Bibliographic record
Abstract
Data repository accompanying the paper 'Optimised DNA isolation from marine sponges for natural sampler DNA (nsDNA) metabarcoding' by Harper et al. (2022). 1_Raw_Data.zip This zipped folder contains the Jupyter notebook and sample_accessions.tsv file required to download raw illumina data from the NCBI Sequence Read Archive: BioProject: PRJNA854174 BioSample accessions: SAMN29421799 - SAMN29421894 (Phase 1) and SAMN29444657 - SAMN29444784 (Phase 2) SRA accessions: SRR19906974 - SRR19907069 (Phase 1) and SRR19912485 - SRR19912613 (Phase 2) 2_Reference_Databases.zip This zipped folder contains scripts used to generate curated reference databases used for taxonomic assignment in GenBank/fasta format. 3_Tapirs.zip This zipped folder contains the scripts and files needed to perform bioinformatic processing with Tapirs. In order to make use of scripts, you will have to install Tapirs and its dependencies. Please see the Tapirs GitHub repository for instructions on how to do this. 4_Data_Analysis.zip This zipped folder contains all scripts and metadata required to produce figures and statistically analyse data in R. Please contact Dr Lynsey Harper (lynsey.harper2@gmail.com) if you encounter any issues!
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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.128 |
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".