Analytical methods and quality assurance
Bibliographic record
Abstract
A laboratory information management system (LIMS) written in Visual Basic using Microsoft Access 95 database and operating in the Windows 95 environment has been developed. The system provides easy entry of all test parameters including sample set identification, sample type, date samples taken and received, sample disposal procedures, identification of technicians responsible and individual sample identification. The system allows input of ASCII files from intelligent laboratory instruments. Data, including reports, can be easily imported or exported in text or ASCII format. Printable customized laboratory work sheets and final reports are designed using parameters stored in a separate protected but editable database. Minimal operator input is required for the calculation and electronic mailing of results, as well as the storage and archival of all data. Report files can be delivered rapidly using email. An up‐to‐date list of analyses pending is available to the laboratory manager providing information required to plan laboratory activities such as staffing and consumables purchasing. The system provides easy sample tracking.
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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".