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 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) | 0.000 | 0.000 |
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 teacher head, 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".