Bibliographic record
Abstract
accelerating methods 133 ACF (average capacity factor) 159 ACI (annual capital investment) or AI (annual investment) 229, 293, 295 ACHL [average customer hours lost (per event)] 289, 290 active failure 94, 263 adequacy indices 86 ADLC (average duration of load curtailments) 87, 99, 107 annuity method 135 A posteriori test 33 AR (autoregression) 30 ARMV (autoregression moving average) 30 ATC (available transfer capability) 5 autocorrelation function 29 benefi t/cost analysis 139, 232, 251 BCR (benefi t/cost ratio) 12, 139, 295, 302 binomial distribution 310 breaker stuck condition 95 bus load model 45, 46 CRF (capital return factor) 129, 131, 229 cash fl ow 127, 128, 141 CBM (capacity benefi t margin) 5 CBR (cost/benefi t ratio) 296, 305 CDF (customer damage function) 17, 91, 92 CEA (Canadian Electricity Association) 165, 169 central moments 55 CI (capital investment) 295 CHL (customer hours lost) 285, 289, 290
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.035 |
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; both teacher heads agree on what is shown here.
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".