The artificial intelligence (AI) revolution: How important for scientific work and its reliable sharing
Bibliographic record
Abstract
The artificial intelligence (AI) revolution: How important for scientific work and its reliable sharingArtificial intelligence (AI) is the overarching field that aims to create intelligent machines and systems that can perform tasks that would otherwise require human intelligence.This term was first used in 1956 by American computer scientist John McCarthy. 1 In 1950, the 'Turing test' was developed to assess whether a machine exhibits intelligent behaviour equivalent to, or indistinguishable from, that of a human.In essence, if a human judge cannot reliably distinguish between responses from a machine and a human in a blind interaction, the machine is considered to have passed the test. 2 AI involves creating algorithms and systems that enable computers to learn, make decisions and perform tasks that typically require human intelligence, such as problem-solving, learning, natural language understanding and pattern recognition.AI systems can be rule-based (following a set of pre-defined instructions) or learningbased (adapt and learn from data).Under the umbrella of AI, machine learning (ML) focuses on designing algorithms that enable computers to learn from and make predictions or decisions based on data, without explicit
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".