Bibliographic record
Abstract
Introduction As the total value of all daily stock transactions on the Vancouver Stock Exchange kept rising in 1982 and 1983, the exchange's index kept falling. How could this be happening? The cause of the divergence between the mathematical and computed averages consisted in an erroneous algorithm to round the last digits during the computation of the index. Public documents, e.g., Quinn's article [2] include the following pieces of information. The index is the arithmetic average of the selling prices of the nearly 1400 stocks listed on the Vancouver Stock Exchange. The computation of the index started in January of 1982, with the index then pegged to 1000. In about November of 1983, exchange officials estimated that the index should have been at least at 900 and perhaps above 1000, but the computed value of the index was down near 520. The index was computed every time the price of a stock changed, which occurred about 2800 times per day. The computer carried a total of eight decimal digits during the computation, but it truncated the last two digits to display and record the index with only three decimal digits past the decimal point. Thus, if it computed the value 540.32567, then it would record 540.325 for the index. The magnitude of the discrepancy-about 520 instead of 1000 or so-indicated that the cause involved more than only an erroneous rounding of an otherwise correct computation. Indeed, with a computer carrying eight decimal digits, the relative rounding error caused by each addition cannot exceed one half of one unit in tlhe last digit, which is (1/2) X 101-8. In the worst case, if all 1399 additions of all 1400 stock prices suffered from the maximum relative error, errors would compound to at most
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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 | 0.004 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.124 | 0.109 |
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".