Application of quality control in ICR data capture 2001 Canadian census of agriculture
Bibliographic record
Abstract
Intelligent Character Recognition (ICR) has been widely used as a new technology in data capture processing. It was used for the first time at Statistics Canada to process the 2001 Canadian Census of Agriculture. This involved many new challenges, both operational and methodological. This paper presents an overview of the methodological tools used to put in place an efficient ICR system. Since the potential for high levels of error existed at various stages of the operation, Quality Assurance (QA) and Quality Control (QC) methods and procedures were built into this operation to ensure a high degree of accuracy in the captured data. This paper describes these QA / QC methods along with their results and shows how quality improvements were achieved in the ICR Data Capture operation. This paper also identifies the positive impacts of these procedures on this operation. 1. Walter Mudryk and Hansheng Xie, Business Survey Methods Division, Statistics Canada, Ottawa, Canada K1A 0T6.
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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 | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".