Bibliographic record
Abstract
Chance Constrained ProgrammingThe philosophy of the previous methods of stochastic programming was to ensure feasibility of the problem through the second-stage problem at a certain penalty cost.In the chance-constrained approach, some of the problem constraints are expressed probabilistically, requiring their satisfaction with a probability greater than a desired level.This approach is particularly useful when the cost and benefits of second-stage decisions are difficult to assess as the use of second-stage or recourse actions is avoided.These intangible components include loss of goodwill, cost of off-specification products and outsourcing of production.For a typical linear programming model: Min x c T x subject to Ax b; x 0 ðB:1Þassume that there are uncertainties in the matrix A (left-hand-side coefficient) and in the right-hand-side vector b and the above constraint must be satisfied with a probability p 2ð0; 1Þ.Then the probabilistic model can be expressed as follows:Min x c T x subject to PðAx bÞ p; x 0 ðB:2ÞIf we consider a single constraint, for the sake of simplicity, then the above becomes Pða t x bÞ p.Furthermore, assume the randomness is only in the right-hand-side with a distribution of F. When FðbÞ ¼ p, then the constraint can be written as Fða t xÞ p ! a t x b.In this case, the model yields a standard linear program.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.256 | 0.046 |
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".