Pair Selection Optimization for InSAR Time Series Processing
Bibliographic record
Abstract
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Geophysical Research - Solid Earth. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Pair Selection Optimization for InSAR Time Series ProcessingAuthorsDelphineSmittarelloNicolas F.d'OreyeiDMaximeJaspardDominiqueDerauwiDSergey VSamsonoviDSee all authors Delphine SmittarelloCorresponding Author• Submitting AuthorEuropean Center for Geodynamics and Seismologyview email addressThe email was not providedcopy email addressNicolas F. d'OreyeiDNational Museum of Natural History of Luxembourg and European Center for Geodynamics and SeismologyiDhttps://orcid.org/0000-0002-3192-448Xview email addressThe email was not providedcopy email addressMaxime JaspardEuropean Center for Geodynamics and Seismologyview email addressThe email was not providedcopy email addressDominique DerauwiDCentre Spatial de Liège and Universidad Nacional de Rio NegroiDhttps://orcid.org/0000-0002-8354-100Xview email addressThe email was not providedcopy email addressSergey V SamsonoviDNatural Resources CanadaiDhttps://orcid.org/0000-0002-6798-4847view email addressThe email was not providedcopy email address
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".