Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data
Bibliographic record
Abstract
Abstract This study deals with the analysis of temporal gravity variations in central Poland, deduced from multiple sources and covering the period from 2002–2016. The gravity data sets used comprise GRACE monthly solutions, GLDAS land surface models and absolute gravimeter measurements from the FG-5 gravimeter located in Józefosław, Poland. All data are corrected using standard processing methods in order to include the same gravity effects. After removing the annual and semi-annual components from all data using least-squares spectral analysis and seasonal-trend decomposition, the deseasoned time series are derived and examined for signatures of extreme hydrological events. The signatures of several severe drought and flood conditions affecting Poland and central Europe are identified. A complementary correlation analysis is performed to assess the level of agreement between different data sources. A higher correlation is shown when the analysis is restricted in the 2009–2012 period that includes the 2010 extreme flood and 2011 increased precipitation events, both affecting Poland.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".