<i>baRcodeR</i> : An open‐source R package for sample labelling
Bibliographic record
Abstract
Abstract Repeatable experiments with accurate data collection and reproducible analyses are fundamental to the scientific method but may be difficult to achieve in practice. Open‐source tools aid the reproducibility of data analysis, but analogous tools are generally lacking for sample collection and other early stages of scientific inquiry. We introduce the R package baRcodeR for generating informative identifier (ID) codes with digitally encoded linear or 2D barcodes. Codes can be imported from an existing dataset (e.g. CSV file) or generated rapidly in baRcodeR , producing scannable barcodes with customizable page layouts for printing and scanning with consumer‐grade printers and scanners. User‐defined ID codes may contain a simple sequence (e.g. SAMPLE‐0427 ) or encode more meaningful sample information such as individual subjects, treatment groups, sample populations, time points, spatial coordinates, subsamples or other associations (e.g. Pop22‐Ind08‐Time40 ). In addition to command‐line functions, a graphical‐user‐interface (GUI) is available from the ‘Addins’ menu of R Studio or online at https://bit.ly/baRcodeR to assist with ID code and barcode label creation. baRcodeR can help biologists apply principles of open and reproducible science to collect and manage biological samples.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.114 | 0.138 |
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".