Discrete wavelet transform based steam detection with Adaboost
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
Steam can cause occlusion in many object detection applications, such as the real problem of large lump detection (LLD) in oil sands mining which motivated our work. In this paper, we propose a general method to overcome this steam detection problem. The existing steam detection methods feasible for our application generally extract features from the transformed input image first and then feed them to a classifier in a completely independent step. In these methods, the step of feature extraction is usually cumbersome and application-dependent. Therefore, we propose a new steam detection method by feeding directly the transformed image to an Adaboost classifier. By doing so, we discard the considerable computational load normally dedicated to feature extraction and benefit from the accuracy of the proper classifier built by Adaboost. Finally, experiments on steam and smoke data sets demonstrate that the proposed steam detection method outperforms the competing methods when taking both efficiency and accuracy into account.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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