The Confounding Effects of Ability, Item Difficulty, and Content Balance Within Multiple Dimensions on the Estimation of Unidimensional Thetas
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
Sixty six samples of red and white wine from Ontario (VQA), British Columbia (VQA), Québec ("vins artisanaux"), imported wines (from Italy, South America and USA) and Canadian and US grape and cranberry juices were analysed for theAlternaria mycotoxins alternariol (AOH) and alternariol monomethyl ether (AME). After cleanup on aminopropyl SPE columns, AOH and AME were initially determined by reversed phase LC with UV detection. Positive sample extracts were re-analysed by LC-tandem negative ion electrospray mass spectrometry (MS/MS) in multiple reaction mode. Overall mean method recoveries measured by LC-UV were 93% for AOH and 81% for AME. Limits of detection in wine (and juice) by LC-UV for AOH were 0.8 (0.4) ng/ml and for AME were 0.5 (0.4) ng/ml; they were below 0.01 ng/ml by LC-MS/MS. As determined by LC-MS/MS, AOH was found in 13/17 Canadian red wines at levels of 0.03 to 5.02 ng/ml and in 7/7 imported red wines at 0.27-19.4 ng/ml, usually accompanied by lower concentrations of AME. Red grape juices (5 positive/10 samples) contained only sub ng/ml levels of AOH or AME except for one sample (39 ng AME/ml). White wines (3/23 samples), white grape juices (0/4 samples) and cranberry juices (1/5 samples) contained little AOH/AME (≤1.5 ng/ml).
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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