Bibliographic record
Abstract
SAS®9.3 Christopher Battiston, SickKids, Toronto, Canada INTRODUCTION SAS® is one of the most versatile software packages available on the market today; with it you can analyse everything from genetics to market research, financial to quality and risk data all using variations of SAS. However, with that versatility comes a price sometimes there is something you absolutely have to do and cannot seem to find an easy way of doing it. When that happens, it is as if the earth stops; What do you mean I can't do this in SAS? you ask yourself. You spend sleepless nights online, looking at every paper Lex Jansen has, convinced someone has done what you're trying to accomplish. In some cases you may find that obscure paper with the lines of code you have been so desperately searching for. Sometimes, you don't then comes a hard decision; do you figure out a way of accomplishing your seemingly impossible task in SAS® or do you (gasp) go elsewhere? This was the decision I was forced to make, and I went with SAS® (obviously, because I am writing about it!). The dilemma how to make a data dictionary for an Oracle database, where PROC
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 | 0.016 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.113 | 0.141 |
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".