Bibliographic record
Abstract
In the modern era, artificial intelligence (AI) is deemed to be at the forefront of technological advances. AI is a radical technology that finds its way into every aspect of life. A basic definition of AI is computational designs (and algorithms) that search deep within a huge mass of information and strategically analyze it to infer intelligent conclusions and help the researchers (heavy data miners) in making important decisions. Computational sustainability is the concept of the application of AI to develop computational tools that can be applied to various natural environments to provide sustainable solutions. The various stages of development of tools in the realm of computational sustainability include data acquisition, data interpretation, model fitting, solution optimization, solution execution and feedback validation. Computational sustainability concepts have been widely used in ecological preservation and studying population dynamics. There are many different sectors where applications of AI can be readily found, such as healthcare, food security, transportation, public safety, human resources, education and the automation industry. Applications of AI are radically transforming the mode of various services. Technology as radical and powerful as AI must come with a note of caution regarding the way the data are used, ownership and authorization issues. Large amounts of public awareness, judicial discourse and moral-ethical policing are required to exploit the true potential of this technology.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".