Surface Water and Ocean Topography (SWOT) L2_HR_PIXC data processing for lakes
Bibliographic record
Abstract
Included Files This package includes code for two functional modules, a python envrionment file, along with a standalone executable file. Below is a detailed user guide to help users get started quickly. Executable File (SWOTPIXCProcesser.exe) The archive contains an executable named SWOTPIXCProcesser.exe, which is a compiled version of Function 1 (source code is also provided—see the “Function 1: SWOT PIXC Processing” section below). This executable is designed for users without Python programming experience. It allows users to process SWOT PIXC data and extract Water Surface Elevation (WSE) without setting up a Python environment or installing dependencies. Input parameters is summarized below. Input folder: Folder path, SWOT L2 PIXC data in NetCDF (.nc) format Output folder: Folder path, SWOT L2 PIXC data in .nc format into geolocated points Name (9 characters required): Enter a 9-characters name Lake polygon: Esri Shapefile path, lake boundaries to be processed; can include multiple lake polygons Lake polygon buffer: Esri Shapefile path, lake boundaries buffer zone to be processed; can include multiple polygons Points within lake folder: Folder path, Geolocated PIXC points that fall within lake polygons Final result folder: Folder path, lake WSEs Quick Deployment (Python Environment) This project provides a env.yml file that specifies the Python environment and all required packages. You can create and activate the environment using the following command: conda env create -f env.yml We strongly recommend using this environment to ensure full compatibility with the provided code. Function 1: SWOT PIXC Processing This module includes a script named main.py and two supporting files, nc2shpfile.py and select_points_code0605.py, which contain the necessary functions for data processing. You can modify the data path at the end of main.py to match your file location. A summary of input parameters are listed below: input_folder = 'Folder path, SWOT L2 PIXC data in NetCDF (.nc) format' output_folder = 'Folder path, SWOT L2 PIXC data in .nc format into geolocated points' point_shp_path = 'Folder path, the geolocated points to be processed' polygon_shp_path = 'Esri Shapefile path, lake boundaries to be processed; can include multiple lake polygons' polygon_shp_buffer_path = 'Esri Shapefile path, lake boundaries buffer zone to be processed; can include multiple polygons' result_shp_path = 'Folder path, Geolocated PIXC points that fall within lake polygons' final_result_path = 'Folder path, lake WSEs' Function 2: Layover Intersection Model (LIM) based on observation geometry This module also includes a main_layover.py script, together with the fishnet_statistic.py, extractDEMvalue.py, and ctsV2.py function files. The LIM code estimates the potential layover contribution from multi-terrain scattering by analyzing SWOT PIXC pixels under their incidence angles. A summary of input parameters are listed below: polygon_shp = 'Esri Shapefile path, lake boundaries to be processed' point_shp_folder = 'Esri Shapefile path, Geolocated PIXC points that fall within lake polygon' output_shp_folder = 'Folder path, fishnet Shapefile created' line_shp = 'Esri Shapefile path, SWOT orbit file in Ersi polyline' dem_path = 'GeoTIFF file path, DEM in GeoTIFF format' output_shp2_folder = 'Folder path, layover intersection'
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.219 | 0.141 |
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".