AI based Framework for Sustainable Business Management using Machine Learning Models
Bibliographic record
Abstract
In this study, the researchers investigate the mutually beneficial relationship that exists between machine learning (ML) and big data, highlighting both the benefits and the challenges that are brought about by the combination of these two domains. Large datasets provide a difficult environment for machine learning (ML) algorithms to extract meaningful insights from, but ML also provides sophisticated tools to assist in navigating through the complexity of these datasets. Even while there is the potential for synergy, their full-fledged adoption is delayed by significant constraints such as the quality of the data, the need for computing, concerns around privacy, and the skills gap. In addition to highlighting recent advancements in algorithmic efficiency, data preparation, and ethical frameworks, this paper investigates the strategic strategies and techniques that are used to address these issues. An in-depth investigation of machine learning models on a variety of datasets is presented, which offers insights into the efficacy of these models and their applicability for instances that occur in the real world, particularly those that involve the pharmaceutical industry. The research sheds light on the ways in which the environment is undergoing transformations and the ways in which practitioners are actively searching for and putting these challenges into practice in order to fully grasp the revolutionary potential of big data and machine learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 | 0.003 |
| 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 teacher head, 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".