Trend. Xignite. FactSet Corporate Fundamentals: Float | Country: USA | Stock Symbol: ISXIF | Symbol Name: iShares S&P BSE Units | Exchange: Other OTC/NBB, Day Format Error: 2147483647-Day Format Error: 0. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 016-005-072.
Bibliographic record
Abstract
Xignite (2017). FactSet Corporate Fundamentals: Float | Country: USA | Stock Symbol: ISXIF | Symbol Name: iShares S&P BSE Units | Exchange: Other OTC/NBB, . Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 016-005-072. Dataset: Returns the annual shares outstanding less closely held shares (float) for the fiscal year for the date(s) requested. This database provides stock fundamentals data for active United States and Canadian listed companies. Companies are identified by symbol and name. Equities, common stock, and some ETFs and preferred stocks are covered. Exchanges include BATS, NASDAQ, NYSE ARCA, NYSE Market Equities, and limited OTCBB and TSX Venture coverage (liquid companies only). Data points include earnings and dividends, ratios and sales, and stock price. Note that not all metrics are available for all stocks. Data-Planet distributes the data, produced by FactSet (www.factset.com), via license from Xignite. FactSet was founded in 1978 and has been publicly held since 1996. The Company is dual listed on the New York Stock Exchange and the NASDAQ Stock Market under the symbol “FDS.” Category: Industry, Business, and Commerce, Banking, Finance, and Insurance 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/ Subject: Corporations, Financials
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.214 | 0.342 |
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".