7th Annual Earth System Grid Federation Face-to-Face Conference Report
Bibliographic record
Abstract
The Seventh Annual Earth System Grid Federation (ESGF) Face-to-Face (F2F) Conference held December 4–8, 2017, in San Francisco, California, USA, assembled together a collection of independently funded national and international projects comprised of government agencies, institutions, and companies dedicated to the creation, management, analysis, and distribution of extreme-scale scientific data. The purpose of the conference was to discuss sustaining and enhancing the resilient ESGF data infrastructure with friendlier tools for the expanding global scientific community—this year’s emphasis was placed on the preparedness of the Coupled Model Intercomparison Project, phase 6 (CMIP6). It also focused on new tools that fulfill important and strategic capability gaps in scientific data archiving, access, analysis, and knowledge discovery. As the Executive Committee Chair of ESGF, I would like to personally thank each of conference attendees and those who could not attend but contributed to and/or supported the development of the ESGF software stack. It is an exciting time for the ESGF consortium as we continue to grow and adjust, remaining always adaptable, motivated, and responsive to our growing base of community projects. As we move forward, our ESGF organization is confronting and addressing many changes during a time of larger national and international commitment with fewer community resources. That said, our commitment to our sponsors and the community remains strong as we continue to meet the challenges before us and bring inspired developers and the scientific community together through forums like this conference, ensuring our ESGF organization remains robust and at the cutting edge of technology.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.133 | 0.085 |
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".