Evaluation and Analysis of Uncertainty on Lake Elevation Measurement Performance of GEDI in the U.S. and Canada
Bibliographic record
Abstract
GEDI (Global Ecosystem Dynamics Investigation) data was analyzed by orbit and by each lake to understand waveform characteristics and define filtering criteria for improved water surface elevation measurement. The L2A data were filtered using guidance provided in the L2A Algorithm Theoretical Basis Document (ATBD). Further adjustment and filtering were applied to identify outliers and improve the accuracy of GEDI elevation measurement. The daily, weekly, and monthly time series of lake elevation were compared at each lake for each of the eight GEDI beams. For seven lakes in North America, GEDI elevation estimates exhibited an overall good agreement with in-situ water levels from local gauges with a mean elevation bias of 0.23 m and Pearson correlation coefficient of 0.70. The comparison of GEDI elevations to those from the HYDROWEB database had a mean elevation bias of 0.12 m and correlation coefficient of 0.61. Over the seven lakes, the bias between GEDI elevations and in-situ data ranged from -0.10 m to +0.53 m with a correlation coefficient ranging from 0.52 to 0.84. The bias between GEDI elevations and satellite-based data ranged from -0.31 m to +0.37 m with a correlation coefficient ranging from 0.38 to 0.82. This work emphasizes the feasibility of GEDI data for accurately calculating lake water levels and has the potential to further our knowledge of lakes' hydrological significance, particularly in the data limited area in the world.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".