Bibliographic record
Abstract
What's Changed Enhancement: Don't index horizons by subproblems by @anamileva in https://github.com/blue-marble/gridpath/pull/1090 Feature: Hydro energy budgets by stage by @anamileva in https://github.com/blue-marble/gridpath/pull/1091 added original RA Toolkit pre- and post-processing code by @elainekhart in https://github.com/blue-marble/gridpath/pull/1093 Enhancement: Reimplement Tx targets to be indexed by horizon by @anamileva in https://github.com/blue-marble/gridpath/pull/1067 Feature: Max transmission targets by @anamileva in https://github.com/blue-marble/gridpath/pull/1069 Feature: Project-level carbon credit purchases by @anamileva in https://github.com/blue-marble/gridpath/pull/1075 Bugfix: Fix issues when updating existing subscenario data in the database by @sriharid in https://github.com/blue-marble/gridpath/pull/1085 Feature: Carbon credits exogenous demand and supply by @Janie115 in https://github.com/blue-marble/gridpath/pull/1081 Major Feature: RA Toolkit Integration by @anamileva in https://github.com/blue-marble/gridpath/pull/1094 Bugfix: Fix the validation of heat rates by @Janie115 in https://github.com/blue-marble/gridpath/pull/1096 Enhancement: Run individual RA Toolkit steps from main script by @anamileva in https://github.com/blue-marble/gridpath/pull/1100 Enhancement: OS-dependent test example objective functions by @anamileva in https://github.com/blue-marble/gridpath/pull/1103 New Contributors @elainekhart made their first contribution in https://github.com/blue-marble/gridpath/pull/1093 Full Changelog: https://github.com/blue-marble/gridpath/compare/v0.16.1...v2024.1.0
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.418 | 0.535 |
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".