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
Abstract As offshore oil and gas developments increase in northern areas such as the Grand Banks and the Arctic region, the operators face challenging conditions. Icebergs are among one of the challenges for both surface and subsea structures if they drift toward those facilities. Prediction of the iceberg drift and dynamic response to any towing process requires a good understanding of hydrodynamic effects induced by currents, waves, tow lines, etc. A reasonable estimation of added mass and RAOs are other prominent parameters required when modeling iceberg dynamics is of interest. Having access to the high resolution full 3D iceberg profiles collected in 2012 (Younan et al. 2016), it is now possible to investigate iceberg hydrodynamics using numerical and experimental methods. This paper presents an overview of the numerical simulation results and lessons learned during various hydrodynamic simulations such as decay analysis, towing, and iceberg-structure interaction. The Diffraction Model and Computational Fluid Dynamics (CFD) are the tools utilized in these simulations. The conclusions provide key findings and suggestions for future analysis of iceberg hydrodynamics.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it