Omissions, Ambiguities In, And Misinterpretations Of 1D Regional Earth Conductivity Models On Which Nerc Tpl-007-1 Table 3 "Beta" (Β) Scaling Factors Rely
Bibliographic record
Abstract
Abstract. NERC’s TPL 7 (see Glossary, Page 14) ground conductivity (β) scaling factors rely on Earth conductivity estimates assembled quickly for EPRI in 20 1D models by Dr. Peter Fernberg (“an independent researcher from Ottawa”) and published in 2012. These default scaling factors and their underlying 1D models suffer from omissions, ambiguities, misinterpretations — identified here and tabulated in Appendix A — which merit attention because TPL 7 allows planners for regulated entities to use different “specific earth model(s) with documented justification.” This paper does not address pros and cons of 1D, 2D, 3D models and their assumptions. This paper may support improvement of Earth conductivity models, a high priority for NERC.
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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 | 0.014 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".