Additional file 1 of Reference intervals for hemoglobin and mean corpuscular volume in an ethnically diverse community sample of Canadian children 2 to 36 months
Bibliographic record
Abstract
Additional file 1: Table A1. Sample size, skewness and p-values from a normality test for each of the partitions each corresponding to Table 2. Table A2. Methods used for estimating the lower and upper limits of the reference intervals provided in Table 2, where P, NP and R represent the parametric, non-parametric and robust methods. The methods are selected according to sample size and distributional investigations provided in Table A1. Table A3. Sample size, skewness and p-values from a normality test for each of the partitions each corresponding to Table 3. Table A4. Methods used for estimating the lower and upper limits of the reference intervals provided in Table 3, where P, NP and R represent the parametric, non-parametric and robust methods. The methods are selected according to sample size and distributional investigations provided in Table A3. Table A5. Sample size, skewness and p-values from the test of normality corresponding to partitions used in Table 4. Table A6. Methods used for estimating the lower and upper limits of the reference intervals provided in Table 4, where P, NP and R represent the parametric, non-parametric and robust methods. The methods are selected according to sample size and distributional investigations provided in Table A5. Table A7. Sample size for monthly age partitions for females and males.
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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 | 0.001 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.647 | 0.046 |
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".