Bibliographic record
Abstract
Classification is a task that involves making a prediction about which class(es) a data point belongs to; this data point can be text, an image, audio, or can even be multimodal.This task can become intractable for many reasons, including:• Insufficient training data to create a data-driven model; available training data may not be appropriate for the domain being studied, it may not be of the right type (e.g.only text but you want text and images), it may not have all of the categories you need, etc.• Lack of available annotators with domain expertise, and/or resources such as time and money to label large amounts of data.• Studying a phenomenon that changes rapidly, so what constitutes a class may change over time, making the available training data obsolete.ADaPT-ML (Figure 1) is a multimodal-ready MLOps system that covers the data processing, data labelling, model design, model training and optimization, and endpoint deployment, with the particular ability to adapt to classification tasks that have the aforementioned challenges.ADaPT-ML is designed to accomplish this by:• Using Snorkel (Ratner et al., 2020) as the data programming framework to create large, annotated, multimodal datasets that can easily adapt to changing classification needs for training data-driven models.• Integrating Label Studio (Tkachenko et al., 2020(Tkachenko et al., -2021) ) for annotating multimodal data.• Orchestrating the Labelling Function / Label Model / End Model development, testing, and monitoring using MLflow (Chen et al., 2020).• Deploying all End Models using FastAPI (Ramírez, 2021)
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.021 |
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".