The Study of Semantic Analysis on Intelligence Research under the Environment of Big Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Faced with complex, large mass of data, how to find the information we need from these data, then to do intelligence research, it is an issue of concern in the intelligence community. This paper analyzes the significance of research and three technologies to ensure the rigor of intelligence research: visualization, data mining and semantic analysis technology, focuses on the semantic analysis technology in the application of intelligence research, exemplified by the semantic role annotation and semantic-based text orientation analysis of two methods, described the meaning of these two methods, the semantic database, the basic flow of information, their strengths and weaknesses, as well asdevelopment and raised its outlook in information research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.020 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it