Reflections on the CLIVAR Early Career Scientists Symposium 2016
Bibliographic record
Abstract
Abstract We present a summary report of the CLIVAR Early Career Scientists Symposium, a three-day event associated with the CLIVAR Open Science Conference held in Qingdao, China during September 2016. The Symposium aimed to capture the ideas of early career researchers on pressing science priorities, imminent challenges, and emerging opportunities to help guide the future evolution of CLIVAR. We identified the need for improving process-based understanding and predictability of regional climate variability and change, moving toward seamless predictions, and improving and expanding global observations. We emphasize the need for increasingly open science, including universal access to data, code, and publications as well as opportunities for international cooperation and exchange. As the next generation of climate scientists, we are dedicated to overcome the challenges outlined in this summary and are looking forward to advancing CLIVAR’s mission and activities.
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 | 0.085 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.025 | 0.035 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".