Automatic algorithm to classify and locate research papers using natural language
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
The objective of this paper was to provide an automatic engine to classify and locate information using natural language. The proposal integrates a set of two algorithms to extract information from different repositories using their own open APIs and creates a knowledge database using a natural language approach using a Bayesian algorithm to classify and a second algorithm to clean the paper. Putting said techniques together derived in a strong alternative which reach common gaps in classification and location of information including avoid the use of the whole paper to get information and not only the information introduced at the moment of upload the paper in the digital library. The proposal was oriented to classify and locate research papers in order to better describe this contribution, however, findings could be applicable to a vast range of scenarios. An adaptation of the popular methodology Crisp-DM was used to evaluate the performance of the algorithm obtaining good results in classifying, searching, and feeding the knowledge base.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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