The Molecular Data Organization for Publication (MDOP) R package to aid the upload of data to shared databases
Bibliographic record
Abstract
Molecular identification methods, such as DNA barcoding, rely on centralized databases populated with morphologically identified individuals and their referential nucleotide sequence records. As molecular identification approaches have expanded in use to fields such as food fraud, environmental surveys, and border surveillance, there is a need for diverse international data sets. Although central data repositories, like the Barcode of Life Datasystems (BOLD), provided workarounds for formatting data for upload, these workarounds can be taxing on researchers with few resources and limited funding. To address these concerns, we present the Molecular Data Organization for Publication (MDOP) R package to assist researchers in uploading data to public databases. To illustrate the use of these scripts, we use the BOLD system as an example. The main intent of this writing is to assist in the movement of data, from academic, governmental, and other institutional computer systems, to public locations. The movement of these data can then better contribute to the global DNA barcoding initiative and other global molecular data efforts.
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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 | 0.016 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.164 | 0.108 |
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".