Bibliographic record
Abstract
Knowledge of the present-day crustal stress field is a key for the understanding of geodynamic processes such as global plate tectonics and earthquakes. It is also essential for the management of geo-reservoirs and underground storage sites. Since 1986, the World Stress Map (WSM) project has systematically compiled the orientation of maximum horizontal stress (SHmax). It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources and it is maintained at the German Research Centre for Geosciences GFZ. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and global comparability. Further information on the WSM project are provided at http://www.world-stress-map.org. The displayed data is a new compilation and all data records have been checked. The total number of data records increased from 1406 in the WSM 2016 to 2388 in this release. The digital version of the stress map is a layered pdf where also the mean SHmax orientation on a 1° grid is provided. It is estimated with the script stress2grid (Ziegler and Heidbach, 2019) using search radii of 100 und 200 km, respectively. The mean SHmax orientation is only estimated when n > 5 data records are located within the search radius.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.047 |
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 source (direct Gemma or distilled Codex), 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".