Bibliographic record
Abstract
This chapter contains sections titled: Future Loss Rate Prediction: Ships and Tsunamis Predicted Insurance Rates for Shipping Losses: Historical Losses The Premium Equations Financial Risk: Dynamic Loss and Premium Investments Numerical Example Overall Estimates of Shipping Loss Fraction and Insurance Inspections The Loss Ratio: Deriving the Industrial Damage Curves Making Investment Decisions: Information Drawing from the Jar of Life Information Entropy and Minimum Risk Progress and Learning in Manufacturing Innovation in Technology for the Least Product Price and Cost: Reductions During Technological Learning Cost Reduction in Manufacturing and Production: Empirical Elasticity, Power Laws and Learning Rates A New General Formulation for Unit Cost Reduction in Competitive Markets: the Minimum Cost According to a Black-Scholes Formulation Universal Learning Curve: Comparison to the Usual Economic Power Laws The Learning Rate b-Value Elasticity Exponent Evaluated Equivalent Average Total Cost b-Value Elasticity Profit Optimisation to Exceed Development Cost The Data Validate the Learning Theory Non-Dimensional UPC and Market Share Conclusions: Learning to Improve and Turning Risks into Profits References
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".