continuing with the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1955), the Wechsler
Bibliographic record
Abstract
This study developed regression algorithms for estimating IQ scores using the Canadian WAIS-III norms. Participants were the Canadian WAIS-III standardization sample (n = 1105). The sample was randomly divided into two groups (Development and Validation groups). The Development group was used to generate 12 regression algorithms for FSIQ and three algorithms each for VIQ and PIQ. Algo-rithms combined demographic variables with WAIS-III subtest raw scores. The algorithms accounted for 48–78 % of the variance in FSIQ, 70–71 % in VIQ, and 45–55 % in PIQ. In the Validation group, the major-ity of the sample had predicted IQs that fell within a 95 % CI band (FSIQ = 92–94%; VIQ = 93–95%; PIQ = 94–94%). These algorithms yielded reasonably accurate estimates of FSIQ, VIQ, and PIQ in this healthy adult population. It is anticipated that these algorithms will be useful as a means for estimating premorbid IQ scores in a clinical population. However, prior to clinical use, these algorithms must be validated for this purpose.
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 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".