smallAreaHealthStatisticsUnit/algae: Code for the ALGAE Protocol
Bibliographic record
Abstract
Longitudinal birth cohort studies often assign environmental exposure during pregnancy based on residential address of the mother at the time of birth. This might lead to exposure misclassification due to residential mobility during pregnancy. Inaccurate or incomplete address records in cohort studies may be a further source of exposure misclassification. We developed an ALGorithm for Generating Address Exposures (ALGAE) to provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life-stage specific exposure estimates with the potential to reduce exposure misclassification. This study was supported by the UK Medical Research Council and the Wellcome Trust (Grant ref.: 102215/2/13/2). The work presented here was specifically funded by The UK Medical Research Council: "Effects Of Early Life Exposure To Particulates On Respiratory Health Through Childhood And Adolescence: ALSPAC Birth Cohort Study", Grant Ref: G0700920.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.259 | 0.079 |
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".