Application of quality control in ICR data capture 2001 Canadian census of agriculture
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
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.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it