Vertical deformation rate during 2017-2023 near Ciudad Guzmán, Jalisco, Mexico, computed from Sentinel-1 data using MSBAS software
Bibliographic record
Abstract
Vertical deformation rate during 2017-2023 near Ciudad Guzmán, Jalisco, Mexico, computed from Sentinel-1 ascending (Track 049 Frame 0059) and descending (Track 012 Frame 0526) data using MSBAS software (Samsonov and d‘Oreye, 2017). The results have not been validated. The presence of upward deformation cannot be explained and can be a processing artifact. Files bperp_049_0059 - interferogram baselines for track 049 bperp_012_0526 - interferogram baselines for track 012 MSBAS_LINEAR_RATE_UD - vertical linear deformation rate measured in m/year MSBAS_LINEAR_RATE_STD_UD - standard deviation of the vertical linear deformation rate measured in m/year MSBAS_LINEAR_RATE_R2_UD - coefficient of determination R2 Guzman_time_series - time series of 5x5 pixel region centred at the subsidence extrema at Ciudad Guzmán References Samsonov, S. and d‘Oreye, N. 2017. Multidimensional Small Baseline Subset (MSBAS) for Two-Dimensional Deformation Analysis: Case Study Mexico City. Canadian Journal of Remote Sensing, 43, 318–329, https://doi.org/10.1080/07038992.2017.1344926 Contact See readme.pdf
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".