SeqDB: Biological Collection Management with Integrated DNA Sequence Tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agriculture and Agri-Food Canada (AAFC) is home to a world-class taxonomy program based on Canada’s national agricultural collections for Botany, Mycology and Entomology. These collections contain valuable resources, such as type specimen for authoritative identification using approaches that include phenotyping, DNA barcoding, and whole genome sequencing. These authoritative references allow for accurate identification of the taxonomic biodiversity found in environmental samples in fields such as metagenomics. AAFC’s internally developed web application, termed SeqDB, tracks the complete workflow and provenance chain from source specimen information through DNA extractions, PCR reactions, and sequencing leading to binary DNA sequence files. In the context of Next Generation Sequencing (NGS) of environmental samples, SeqDB tracks sampling metadata, DNA extractions, and library preparation workflow leading to demultiplexed sequence files. SeqDB implements the Taxonomic Databases Working Group (TDWG) Darwin Core standard Wieczorek et al. 2012 for Biodiversity Occurrence Data, as well as the Genome Standards Consortium (GSC) Minimum Information about any (X) Sequences (MIxS) specification Yilmaz et al. 2011. When coupled with the built-in data standards validation system, this has led to the ability to search consistent metadata across multiple studies. Furthermore, the application enables tracking the physical storage of the aforementioned specimens and their derivative molecular extracts using an integrated barcode printing and reading system. All the information is presented using a graphical user interface that features intuitive molecular workflows as well as a RESTful API that facilitates integration with external applications and programmatic access of the data. The success of SeqDB has been due to the close collaboration with scientists and technicians undertaking molecular research involving the national collection, and the centralization of their data sets in an access controlled relational database implementing internationally recognized standards. We will describe the overall system, and some of our lessons learned in building it.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it