Bibliographic record
Abstract
This version bump adds a feature to include user fuels and fixes a number of bugs. Add the option for users to define/assign their own fuels New option for atb_modifiers and modified_gen_atb to directly set parameter values rather than using a python operator Don't force retirement based on age if not specified in settings. Don't retire units unless there is a retirement year before the planning period year -- this is a change from previous behavior, where only units with retirement after the planning year would be kept Add new check for GenX model tags Update example system settings file based on results of check for model tags Expand testing What's Changed Fix fuel name bug through eia/atb tech map by @gschivley in https://github.com/PowerGenome/PowerGenome/pull/158 Check resource tags by @gschivley in https://github.com/PowerGenome/PowerGenome/pull/159 Add option for user fuels and prices by @gschivley in https://github.com/PowerGenome/PowerGenome/pull/163 Updating nrelatb.py by @xuqingyu in https://github.com/PowerGenome/PowerGenome/pull/162 Update GenX.py by @xuqingyu in https://github.com/PowerGenome/PowerGenome/pull/167 Adjust code to match heat rate table with cost_case column by @gschivley in https://github.com/PowerGenome/PowerGenome/pull/166 Format Python code with psf/black push by @github-actions in https://github.com/PowerGenome/PowerGenome/pull/170 User fuels, better tests, don't force age retirement, bug fixes by @gschivley in https://github.com/PowerGenome/PowerGenome/pull/169 New Contributors @github-actions made their first contribution in https://github.com/PowerGenome/PowerGenome/pull/170 Full Changelog: https://github.com/PowerGenome/PowerGenome/compare/v0.5.3...v0.5.4
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.221 | 0.255 |
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".