Bibliographic record
Abstract
Acclimation for supply chains, 130 Accounting, 8 ActiveBuddy Inc, 41 Adaptation strategies, 131-132 Adverse Selection Protection Engine system (ASPEN system), 89 Agent Development Environment (ADE), 119 Aggregators, 81 Alexa (chatbot), 41 Algeria (AI program), 89 Algorithmic trading (AT), 73-74 Algorithms, 156 Alibaba, 73 ALICE, 41 Allo (versatile texting application), 41 Alpaca Forecast AI Prediction Matrix, 82 Amazon, 73 American Express, 164 Andhra Bank's Abhi, 44 ANOVA test, 228, 230-231, 233-234, 236 Anti-Money Laundering (AML), 168, 176 Apple, 41 Application performance management (APMs), 247 Applications, 273 Apriori algorithm, 54 Artificial intelligence (AI), 36, 210 impact of AI on financial services, 73-74 AI-based chatbot, 43 AI-based portfolio management, 80 AI-driven process, 80 AI-fuelled banking chatbot, 43 Algeria, 89 credit decisions and, 80-81 cybersecurity, 76-78 deep learning may be difficult, 80 detection and compliance of fraud, 74-75 Equbot, 88 ethical difficulties, 79-80 in finance, 81 imperative execution, 88-89 implementation challenges, 78 Kavout, 89 management operations, 83-85 method, 72 organisational issues, 78-79 overview of, 73 personalised banking and, 82 and process automation, 82-83 and reduction of fraud, 81 risk management and, 81 robotic advisory services and chatbots, 75-76 robots, 87 Scanz, 86-87 services, 73 Stock Hero, 86 systems, 80 technology of financial product, 80-81 Ticker, 87
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.769 | 0.819 |
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".