Bibliographic record
Abstract
In many areas of statistics and applied mathematics one has to solve the following problem: given a function f (·), which value of x makes f ( x ) as large or as small as possible? For example, in financial modeling f ( x ) might be the expected return from a portfolio, with x being a vector holding the amounts invested in each of a number of possible securities. There might be constraints on x (e.g. the amount to invest must be positive, the total amount invested must be fixed, etc.) In statistical modeling, we may want to find a set of parameters for a model which minimize the expected prediction errors for the model. Here x would be the parameters and f (·) would be a measure of the prediction error. Knowing how to do minimization is sufficient. If we want to maximize f ( x ), we simply change the sign and minimize − f ( x ). We call both operations “numerical optimization.” Use of derivatives and simple algebra often lead to the solution of such problems, but not nearly always. Because of the wide range of possibilities for functions f (·) and parameters x , this is a rich area of computing. The golden section search method The golden section search method is a simple way of finding the minimizer of a single-variable function which has a single minimum on the interval [ a, b ].
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".