TREND: Sharadar. Core US Fundamentals: Sharadar Fundamentals | Symbol: AMZN, AAPL, MSFT | Symbol Name: Amazon Com Inc, Apple Inc, Microsoft Corp | Indicator: Investments, 1993 - 2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 093-002-001
Bibliographic record
Abstract
Sharadar. Core US Fundamentals: Sharadar Fundamentals | Symbol: AMZN, AAPL, MSFT | Symbol Name: Amazon Com Inc, Apple Inc, Microsoft Corp | Indicator: Investments, 1993 - 2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 093-002-001 Dataset: Provides company financials for over 14,000 US public companies. Data points include earnings and dividends, ratios, revenues, assets and liabilities, and more. Note that not all metrics are available for all stocks. Statistics are defined as the “Most-Recent Reported view (MR),” which are time indexed to the most recently reported metrics for the reporting period. These statistics include restatements and are typically suitable for assessing business performance after restatements for mergers/divestitures. Three time dimensions are available: Annual (Year), representing annual observations of one year duration; Trailing Twelve Months, representing quarterly observations of one year duration; and Quarterly, which are quarterly observations of quarterly duration (available only for US domestic companies). The database provides reference grade stock fundamentals data and financial ratios for more than 5,000 active and 9,000 delisted US companies. Foreign issuers (ADRs and Canadian) that trade publicly on US markets and who make SEC filings are also covered. Exchanges include BATS, NASDAQ, NYSE, NYSE Market Equities, and OTC companies. History dates to 1997 for some tickers. Companies are identified by symbol and name, and by trading currency. https://www.quandl.com/databases/SF1/data Category: Industry, Business, and Commerce, Banking, Finance, and Insurance Subject: Corporations, Equities, Stocks, Financials, Publicly Traded Companies Source: Sharadar Sharadar is an independent research and analytics firm founded in 2013. Sharadar specializes in extraction, standardization, and organization of financial data from company filings. https://sharadar.com/
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 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.155 | 0.304 |
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".