Isotope identification using deep learning: An explanation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The exceptional performance of machine learning methods has led to their adaptation in many different domains. In the nuclear industry, it has been proposed that machine learning methods have the potential to revolutionize nuclear safety and radiation detection by leveraging that they can be used to augment human and device capabilities. While many applications focus on the accuracy of the learning algorithm’s prediction, it has been shown in practice that these algorithms are prone to learn characteristics that are not descriptive or relevant. Hence, this paper focuses on understanding the reasoning behind the classification using saliency methods. Visual representations of the network’s learned regions of interest are used to demonstrate whether domain-specific characteristics are being identified, which allows for the end-user to evaluate the performance based on domain knowledge. The results obtained show that focusing on a human-centered approach will ultimately enhance the transparency and trust of the deep learning algorithm’s decision.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it