Primo VE Troubleshooting: Is it Primo? Is it Alma? Is it something else?
Bibliographic record
Abstract
Hear from Primo VE sites all round the world to learn their top tips for troubleshooting Primo VE issues. Sometimes it is tricky to figure out whether the problem is arising in Alma, or is it something you have configured in Primo VE? Sometimes it’s just a plain thorny problem to sort out even if you know where to go. We all need a little help sometimes. If your Library is new to Primo VE, then this session may be especially helpful for you. In a series of lightning talks each presenter will: (1) Describe a problem or two they encountered in Primo VE, (2) Explain how they tracked down the cause, (3) Reveal the outcome. Troubleshooting stories cover integrations, FRBR and Dedup processes, local fields, custom search boxes and working with external data sources.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.197 | 0.082 |
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".