Detection of Differential Item Functioning in the Generalized Full-Information Item Bifactor Analysis Model
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
Carbohydrates make up an important component of our diet, contributing a significant portion to our total caloric intake. The ability to harvest these molecules for energy is reliant on the activity of carbohydrate-active enzymes. Family 31 α-glucosidases are a group of glycoside hydrolases that has been shown to play a key role in the metabolic process of hydrolyzing dietary starch into monomers of glucose. The purpose of the research presented here is to explore evolutionary changes that occurred within this family of glycoside hydrolases, and to relate these divergences to observed structural differences in relation to predicted substrate preferences. Here we report specific single amino acid changes that are believed to have arisen through evolution, and are directly related to the ability of these enzymes to bind different starch-based glycans. Through phylogenetic analysis we observed a number of evolutionary adaptions that we believe resulted in duplicated genes that allow for the efficient utilization of dietary starch.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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