Bibliographic record
Abstract
The Canada in 3D (C3D) project (https://canada3d.geosciences.ca/), formally initiated in the spring of 2020 by the National Geological Surveys Committee (NGSC) is required to provide a working group update to all its provincial and territorial partners. There have been several informal C3D working meetings with the partners prior to the creation of the C3D Charter and there has been a hiatus in communication through the Covid-19 pandemic. To re-engage the C3D community, a video tele-conference was held on June 6th, 2022 with approximately 44 participants. There was representation and presentations of all provinces and territories with various managers, technical and scientific observers. The purpose of this compilation of presentations and discussions from this 2022 C3D-NGSC reconnection meeting is to provide activity information to all participants, and their respective organizations, highlighting current geoscience compilation and modelling efforts in 2D and 3D. The aim is to help identify opportunities for collaboration on data standards, methods, applications and best practices but with the overall goal of working toward the C3D vision, outlined in the C3D charter of an updated 2D and 3D geological map/model of Canada.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".