Bibliographic record
Abstract
This talk, given at the FORCE2018 meeting in Montreal, introduces the reproducibility initiative of the Canadian Open Neuroscience Platform (www.conp.ca). CONP offers a flexible and reproducible approach to publishing academic research, combining transparency, data curation and code sharing to create the reproducible paper of the future. Our main goal is to share analyses in a way that lets readers replicate key figures from a journal article, as well as modify the code of the analyses to explore the impact of arbitrary parameters on those figures. To make it easier to re-run a research analysis, CONP integrates containers, data queries and Jupyter notebooks. Instead of acting as a replacement to traditional journal publishing, the CONP initiative will produce a complementary publication focused on analytical reproducibility and sharing. For each study published through CONP, our team will work with the authors to select elements of the study that capture the most important results and facilitate reproduction and exploration by reviewers and readers. We envision that our pipelines will become the standard in peer-review and remove the overhead that comes with accessing other lab’s data and running their analysis code.
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 | 0.136 | 0.334 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.059 | 0.036 |
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".