Bibliographic record
Abstract
TESPy version 0.9.4 is online coming with two major changes: Units are now available for all component and connection parameters and conversions are handled through pint! A new component PolynomialCompressor is available, that employs methods to process datasheets from manufacturers! All new features and required API changes are listed here: https://tespy.readthedocs.io/en/main/whats_new.html What's Changed Fix/#725 ude objects reassignment for pygmo by @fwitte in https://github.com/oemof/tespy/pull/726 Fix: SimpleHeatExchanger considered in to_exerpy and problem with ttd… by @sertomas in https://github.com/oemof/tespy/pull/728 Fix/#730 convergence check by @fwitte in https://github.com/oemof/tespy/pull/731 Add a Node component, that combines Merge with Splitter by @fwitte in https://github.com/oemof/tespy/pull/733 General docs fixes by @fwitte in https://github.com/oemof/tespy/pull/735 Fix/#744 raise error on duplicated subsystem connection label by @fwitte in https://github.com/oemof/tespy/pull/745 Add a PolynomialCompressor class by @fwitte in https://github.com/oemof/tespy/pull/741 Make tespy compatible with pint units by @fwitte in https://github.com/oemof/tespy/pull/743 Full Changelog: https://github.com/oemof/tespy/compare/v0.9.3...v0.9.4
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.329 | 0.375 |
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".