Bibliographic record
Abstract
Artificial Intelligence (AI) is a branch of computer sciences that uses learning algorithms to calculate probability of outcome by using Bayes theorem and other statistical methods for a given certain input (Fig.1). When the chance of an event occurring is calculated over and over again after adding new data or evidence at each step, the probability can reach the level of near certainty for given inputs. Thousands, even millions of data points are incorporated in calculating posterior probability for predictive analytics. The analytics are input neutral as programs predict the future events irrespective of the type of the data. AI has, thus, blurred the boundaries between the physical, digital, and biological worlds. The initial learning process is considered training where inputs are given to the program already marked for the expected outcome. This training information can either be highly precise or very vague allowing different degrees of freedom to the program but also increasing the burden of training. Once trained an AI algorithm is able to predict or analyze given input to suggest the required outcome with some certainty. This improves with continued training through feedback.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".