Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
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
Bayesian Biclustering by Dynamics (BBCD) is a new clustering algorithm for Steam-Assisted Gravity Drainage (SAGD) oil recovery time series data [[1]Pinto H. Gates I. Wang X. Bayesian Biclustering by Dynamics: a Clustering Algorithm for SAGD Time Series Data.Comput Geosci. 2019; 133104304https://doi.org/10.1016/j.cageo.2019.07.008Crossref Scopus (4) Google Scholar]. In this companion paper the BBCD algorithm is tested on synthetic data, demonstrating use of the algorithm, as well as its robustness, and performance accuracy against Random Agglomeration. Supplementary information includes formulae to calculate analytical steam and oil volume data used as background knowledge for the SAGD application. Advantages of the BBCD algorithm are listed below. •It includes background knowledge directly into the clustering process.•It finds similarity between series and over time.•It allows a user-specified definition for behaviour of interest, which relaxes dependency on series shape. This is important when similar behavioural events do not necessarily occur in the same temporal order.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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