Bibliographic record
Abstract
P ermutations and combinations appear in problems that have more than one answer, where we want to know what all the possibilities are or just how many possibilities there are. For example, a basketball coach may need to select a team of 5 players from the 10 boys on his squad. How many possible teams is that? Is it more than we can reasonably put in a list? If the coach has statistics on the five starting players from a competing team, can he match up his players with them based on height, speed, and experience? You can address these questions with a handful of algorithms for permutations and combinations that are an important part of a Java developer's toolbox. COUNTING PERMUTATIONS A permutation is an ordering of items. For example, we might have three errands to do, with a choice about what order to do them in. If we have to buy groceries, mail a package, and get an oil change, one possible ordering or permutation is {groceries, mail, oil}. Altogether, there are six possible orderings: groceries, mail, oil groceries, oil, mail mail, groceries, oil mail, oil, groceries oil, groceries, mail oil, mail, groceries We can count these choices algorithmically, without necessarily listing them. Notice that once the errand runner completes one of the three errands, there are always two left. After the errand runner completes two errands, there is always one left.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".