How Do We Extract Solutions of Unmet Needs from the Vast Sea of Big Data
Bibliographic record
Abstract
Thanks to the expansion of ICT technologies and growing databases, we are now allowed easy access to much more valuable information and can easily uti-lize it to suit ones needs. But all the information is from the past, is primary, and in an unstructured for-mat which is like only having the basic ingredients be-fore baking a cake. And to precisely satisfy unmet needs, we have to choose the appropriate cooking method. After over 9 years of using an original predic-tive analytics methods using Big Data, VALUENEX continues to provide solutions for R&D and business strategies, and other types of user specified fields or subjects to find the way of the future up to 20 years from now. The feature of this method appears as white space on the Radar Map through visualized data of up to 100,000 text documents. White space on the radar is a form of intangible contents. Analysis around white space shows us the future scene and solutions for unmet needs. Grasping gravity trends, measuring density, and ex-tracting amounts of characteristics of any groups with precision on our plotting radar tells us the objective truth. We call this method view analytics for prediction. By using panoramic view analytics, you may find alternative solutions from an unex-pected group on the radar when you expand your tar-get scope beyond the field you are familiar with. Thus, we show the effectiveness of panoramic view analysis by saving time and providing solutions for unmet needs according to use case studies in the health care technological fields.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".