Bibliographic record
Abstract
Today, we live in the world of internet.With the advancement of technology, the amount of data access has increased too many folds.Internet access now is not only limited to computer devices but can now be easily accessed through mobile devices viz.Smartphones, tablets, PDA's.The internet is now available to every common man, and with its use he fires many queries on servers and uploads or downloads data from the internet.In fact, 90% of the world's data came in existence in the last three-four years, and that too because the internet is readily available to each and every common individual.Of these, much data is being uploaded and queried upon by mobile devices.As the number of devices for Internet access has increased, and so is the number of queries fired by the users on a particular server.The time taken by a query to process totally depends on the complexity involved in joining the tables distributed along the network and finally extracting the desired result out of it.Processing and optimization of various queries in mobile devices involve much join computation among data present at different sites that may be static or mobile which in turn requires much energy consumption.A mobile device has limited energy, so, it must be utilized efficiently.Much research work have been done till now, in the field of mobile computation and making efficient use of energy.However, as the mobile devices possess some asymmetric features, and because of that the old techniques used in distributed databases cannot be applied directly.This paper brings out some methods, to efficiently utilize mobile energy by employing per split semi-join using MapReduce Framework of Hadoop.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".