TREND: Xignite. Worldwide Stock Market Prices - Major Exchanges: Stock Daily Open | Economic Regions/Exchanges: Shanghai Stock Exchange | Exchange: Shanghai Stock Exchange, . Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 016-003-001
Bibliographic record
Abstract
Xignite. Worldwide Stock Market Prices - Major Exchanges: Stock Daily Open | Economic Regions/Exchanges: Shanghai Stock Exchange | Exchange: Shanghai Stock Exchange, . Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 016-003-001 Dataset: Reports opening stock price for companies listed on major global exchanges. This dataset provides historical prices for equities trading on 11 global exchanges. Daily open, close, high, low, and volume statistics are reported for the NASDAQ, New York Stock Exchange, OTC PINK MARKETPLACE, Deutsche Boerse Ag – Frankfurt, Shanghai Stock Exchange, Tokyo Stock Exchange, London Stock Exchange, Hong Kong Stock Exchange, Toronto Stock Exchange, BOVESPA Sao Paulo, and ASX Tradematch. http://www.xignite.com/product/XigniteGlobalHistorical/api/ Category: Banking, Finance, and Insurance Subject: Stock Indexes, Stock Markets, Stocks, Publicly Traded Companies, Stock Prices Source: Xignite Xignite provides on-demand financial market data covering global equities, commodities, currencies, options, fixed income, mutual funds, derivatives, and OTC (“over-the-counter” or those traded in a context other than a formal exchange) instruments. http://www.xignite.com/
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.214 |
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".