Proceedings of the 3rd international conference on Knowledge capture
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
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.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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