The Shocks To Crude Oil Production. Nonparametric Stationarity Analysis For 20 OPEC And Non-OPEC Countries
Bibliographic record
Abstract
Introduction: With recent breakthroughs in organized and amorphous data and rapid growth in analysis methodologies, Artificial Intelligence (AI) is causing a revolution in the health care industry. The value of Artificial Intelligence in health care is becoming recognized at the same time that people are becoming concerned about the models' possible lack of explainability and bias. This discusses the concept of explainable artificial intelligence (XAI), which enhances a system's trustworthiness, resulting in more widespread AI adoption in health care. In this chapter, we discuss several perspectives on explainable artificial intelligence principles, as well as the understandability and interpretability of explainable AI systems, with a particular focus on the health care sector. This chapter uses AI explainability as a way to help build trustworthiness in the medical domain and takes a look at the recent developments in the area of explainable AI, which encourages creativity and at times are necessary in practice to raise awareness. The goal is to teach health care providers about the interpretability and understandability of explainable AI systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".