Bibliographic record
Abstract
T here is A widespread myth that computers, being fast adding machines, do math well. We all know that this is not true but sometimes we believe anyway. Sometimes we forget that the numeric answers we get have high precision but perhaps no accuracy. I know that I should do the error analysis on each and every floating point operation but sometimes I don't. I have always believed in “consumer arithmetic.” (i.e., I don't care how fast I get the wrong answer. I care how fast I get the right answer.) And I really believe that programming language libraries should support math at least as good as high school algebra. When I get a numeric problem, I want the computer to tell me it can get the answer, it can get close, or it can't solve the problem to the accuracy I want given the data I have. What I don't want is a string of digits, which may or may not have any meaning, with no indication if they have meaning or not. I will explore one small corner of the universe and how math happens in the Java language. I use a specific example from a talk I heard on Interval Arithmetic—more on that later. I'll warn you now that I consider myself a user of numbers rather than a mathematician, but we'll definitely do some math along the way.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.028 |
| Insufficient payload (model declined to judge) | 0.019 | 0.025 |
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".