A Notification System Model for Bioinformatics Community of Practice
Bibliographic record
Abstract
Bioinformatics can be considered as a new field of study and it promises a vast exploration area (Carzaniga., Rosenblum., Wolf ., 2001). In order to expedite the maturity in this area, a proper and supportive portal where all researchers could gather and cooperate in conducting their research need to be established. One of the features in a portal that can assist Bioinformatics researchers in performing their work is the ability of the portal to notify. Notification system is a combination of software and hardware to provide a method of distributing message(s) to a set of recipients. The notification messages could assist the recipients in many ways, from time saving and cost saving till life saving. Notification system can be developed with numerous functions depending on the needs and one of the most beneficial functions in research area is notification on the next most relevant knowledge to be read. This type of notification could lessen the researchers’ time in finding the correct thus enhancing their research efficiency. Another type of notification that could assist researchers is the events reminder. Busy and hectic researchers could forget their pack schedule and put total focus on their research while the reminder prompts them when it is time through Windows’ pop up, email and SMS as the means of delivering the messages. Whilst the knowledge management system (KMS) provides a sturdy basic for the Bioinformatics portal as a whole, the agent technology support the operation of the notification system. Agent technology offers great capabilities in ensuring the recipients is notified accordingly through its autonomous, learning and cooperative characteristics. The objective of this project is to build a notification system for Bioinformatics community of practice (CoP). Researchers in this community could utilize this system to make their research process more efficient.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".