Message from the IEEE SmartData-2018 General Chairs
Bibliographic record
Abstract
Welcome to the Fourth IEEE International Conference on Smart Data (IEEE SmartData-2018), which is held July 30-August 03, 2018, in Halifax, Nova Scotia, Canada. It is held as part of the 2018 IEEE Cybermatics Congress, together with other five conferences: The 2018 IEEE International Conference on Blockchain (Blockchain-2018); The 18th IEEE International Conference on Computer and Information Technology (CIT-2018); The 11th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2018); The 14th IEEE International Conference on Green Computing and Communications (GreenCom-2018); The 11th IEEE International Conference on Internet of Things (iThings-2018). On behalf of the Organizing Committee of the IEEE SmartData-2018, we would like to express to all of authors and participants our sincere and warm welcome in Halifax, Nova Scotia “Canada's Ocean Playground”!
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.082 | 0.076 |
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".