The 11th SPE Comparative Solution Project: Submitted Data
Bibliographic record
Abstract
<p> Data submitted to the 11th Society of Petroleum Engineers Comparative Solution Project. Contains the sparse and dense data files of 77 results submitted by 18 participating groups for three cases SPE11A-C. Each zip file contains one such result, where the name spe<i>X</i>_<i>NAME</i><i>Y</i>.zip indicates Result <i>Y</i> for Case SPE11<i>X</i> of Participant <i>NAME</i>. </p><p> Unpacking a result file yields one sparse data file spe<i>X</i>_time_series.csv, several dense data files spe<i>X</i>_spatial_map_<i>TIME</i>.csv, and, optionally, performance data files. A sparse data file contains the evolution of several scalar quantities over time, while a dense data file contains the spatial distribution of several scalar quantities at a particular reporting time step. For more information, see the related publication. </p><p> The results can be processed by the scripts provided in the repository <a href="https://github.com/Simulation-Benchmarks/11thSPE-CSP">github.com/Simulation-Benchmarks/11thSPE-CSP</a>. From the repository's website, access to a Jupyter Hub is enabled that allows to run the scripts on the full dataset. </p>
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".