3-D GPS velocity field and its implications on the present-day post-orogenicdeformation of the Western Alps and Pyrenees
Bibliographic record
Abstract
Abstract. We present a new 3-D GPS velocity solution for 182 sites for the region encompassing the Western Alps, Pyrenees, and southern France. The velocity field is based on a Precise Point Positioning (PPP) solution, to which we apply a common-mode filter, defined by the 26 longest time series, in order to correct for network-wide biases (reference frame, unmodeled large-scale processes, etc.). We show that processing parameters, such as troposphere delay modeling, can lead to systematic velocity variations of 0.1–0.5 mm yr−1 affecting both accuracy and precision, especially for short (< 5 years) time series. A velocity convergence analysis shows that minimum time-series lengths of ∼ 3 and ∼ 5.5 years are required to reach a velocity stability of 0.5 mm yr−1 in the horizontal and vertical components, respectively. On average, horizontal residual velocities show a stability of ∼ 0.2 mm yr−1 in the Western Alps, Pyrenees, and southern France. The only significant horizontal strain rate signal is in the western Pyrenees with up to 4 × 10−9 yr−1 NNE–SSW extension, whereas no significant strain rates are detected in the Western Alps (< 1 × 10−9 yr−1). In contrast, we identify significant uplift rates up to 2 mm yr−1 in the Western Alps but not in the Pyrenees (0.1 ± 0.2 mm yr−1). A correlation between site elevations and fast uplift rates in the northern part of the Western Alps, in the region of the Würmian ice cap, suggests that part of this uplift is induced by postglacial rebound. The very slow uplift rates in the southern Western Alps and in the Pyrenees could be accounted for by erosion-induced rebound.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".