A Systematic Review on Calibration Transfer and Maintenance by Université de Sherbrooke, Faculty of Engineering, Department of Chemical and Biotechnological Engineering
Bibliographic record
Abstract
This registered project documents a systematic review of calibration transfer (CT) and calibration maintenance (CM) methods, with a focus on near-infrared (NIR) spectroscopy and multivariate calibration models used in regulated, real-world analytical settings. The review consolidates and critically analyzes published evidence on how CT/CM approaches are applied, validated, and reported, with the aim of clarifying best practices, identifying gaps, and supporting robust model lifecycle management consistent with Quality by Design (QbD) and Process Analytical Technology (PAT) principles. The project includes structured screening and data-extraction workflows and provides transparent, reusable datasets derived from the review process. The file “SR_pharma” contains the full extraction table for the articles included in the published pharmaceutical-focused systematic review, enabling reproducibility, secondary analyses, and future updates of the evidence base. The file “SR_AFS” contains extracted information from relevant studies and publicly available datasets in agriculture, food, and soil domains that were excluded from the pharmaceutical review by scope; this companion dataset remains valuable for cross-domain comparison, benchmarking, and future calibration transfer research in related application areas. By making both the systematic synthesis and the underlying extraction tables openly accessible, this OSF registration is intended to promote methodological transparency, encourage consistent evaluation practices, and accelerate the development of reliable, implementable calibration transfer and maintenance strategies by both expert chemometricians and non-expert practitioners.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".