TREND: Quandl. Worldwide Stock Market Prices - Exchanges by Region: Stock Daily Close - Unadjusted Prices | Economic Regions/Exchanges: Toronto Stock Exchange | Stock Symbol: MBA, 05/26/2010 - 06/08/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 090-003-007
Bibliographic record
Abstract
Quandl. Worldwide Stock Market Prices - Exchanges by Region: Stock Daily Close - Unadjusted Prices | Economic Regions/Exchanges: Toronto Stock Exchange | Stock Symbol: MBA, 05/26/2010 - 06/08/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 090-003-007 Dataset: Reports closing stock price for companies listed on major global exchanges. Close refers to the price of the last transaction for a given stock at the end of the reference day. Unadjusted figures reflect the price/volume of a stock on that day, with the only adjustments being exchange corrections. This dataset provides historical prices for equities trading on global exchanges by world region. Stock daily open, close, high, and low prices, and volume are reported. Historical depth of the time series varies by exchange and equity. See the technical documentation for detail. Prices are displayed in the local currency of the exchange. Quandl disseminates the data on behalf of Exchange Data International (EDI). https://www.quandl.com/publishers/EDI Category: Banking, Finance, and Insurance Subject: Equities, Stock Markets, Stocks, Publicly Traded Companies, Stock Prices Source: Quandl Launched in 2013, Quandl disseminates provides core financial data from over 500 publishers. The company offers a global database of alternative, financial and public data, including information on capital markets, energy, shipping, health care, education, demography, economics, and society. The company was acquired by Nasdaq in 2018. https://quandl.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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.167 | 0.263 |
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".