Proceedings of the 3rd international conference on Knowledge capture
Bibliographic record
Abstract
It is our great pleasure to welcome you to the Third International Conference on Knowledge Capture - KCap'05. This year's conference continues its tradition of being the premier forum for presenting research results concerning the acquisition and use of knowledge, including knowledge extracted from vast sources of information as well as directly from users. The aim of the conference is to provide a venue in which disparate research communities whose members are interested in efficiently capturing knowledge from a variety of sources can come together to present ideas, exchange research results, and share their enthusiasm and vision with each other. KCap'05 provides a unique opportunity for this to happen.The call for papers attracted 70 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 21 papers covering a wide range of views and perspectives, but all sharing the common theme of an investigation of knowledge. In addition, we are pleased to have two wonderful invited speakers: Pat Hayes, from the Institute for Human and Machine Cognition, University of West Florida; and Carole Goble, from the University of Manchester, UK. This year's conference also includes a poster session, providing an additional time during the conference where researchers can present and discuss their work with each other.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.035 |
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".