TREND: Stevens Analytics. Continuous Futures: Future Daily Open Price | Symbol: -- | Exchange: All Exchanges | Symbol: ED, 02/01/1982 - 10/23/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 095-001-006
Bibliographic record
Abstract
Stevens Analytics. Continuous Futures: Future Daily Open Price | Symbol: -- | Exchange: All Exchanges | Symbol: ED, 02/01/1982 - 10/23/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 095-001-006 Dataset: Reports the open price of futures contracts by market day. Futures contracts are listed by contract and exchange. Data users will find various options for each contract that vary by the roll rule, ie, the date on which successive contracts are spliced together ("roll"); and price rule, ie, the adjustment made to the raw contract prices (if any). Each option is suitable for particular end use cases. For detail, please see the technical documentation. The Stevens Continuous Futures data feed provides a collection of long-term continuous price histories for 78 US and international futures contracts, collectively accounting for over 90% of North American futures trading volume. Continuous futures contracts are artificial instruments constructed by chaining together individual short-term futures contracts in order to create a single long-term history. Data are updated daily, and provide full historical coverage, going back an average of 30 years per contract. https://www.quandl.com/databases/SCF/data Category: Industry, Business, and Commerce, Banking, Finance, and Insurance Subject: Prices, Futures Market, Futures, Contracts Source: Stevens Analytics The firm is a data vendor, headquartered in Canada. https://www.quandl.com/databases/SCF/documentation?anchor=publisher
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 | 0.011 |
| 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 | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.183 | 0.295 |
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".