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Bibliographic record
Abstract
Sales force is a cloud computing service as a software (SaaS) company that specializes in customer relationship relation management (CRM). Salesforce’s services allow business to use cloud technology to better connect with customers to use cloud technology to better connect with customers, partners and potential customers. Salesforce impressed investors recently by crushing third quarter estimates, reporting a third quarter revenue of $4,5 billion – up 33% year over year. As of 2017, Salesforce reportedly had 150,000 companies using their software – among which include Amazon (AMZN) – Get report, Adidas (ADDYY) , ADP (ADP) – get Report , American Express (AXP) – get Report and many , many more. Now a day’s internet has become sophisticated which made the data storage ease, previously we use to store the data in floppy, disk or a cd and it was really hard to find the data once lost. Now, through cloud computing platform it’s pretty easy to store bulk amount of data without causing any damage to it. This project basically revolves around internet, its usage and how it’s used to reduce the human burden.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.769 | 0.765 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".