The Molecular Data Organization for Publication (MDOP) R package to aid the upload of data to shared databases
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
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.
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.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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