Software to Enable Ocean Discoveries: A Case Study With <scp>ICESat</scp>‐2 and Argo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Increased anthropogenic stressors (e.g., warming, acidification, wildfires, and other extreme events) present complex observational challenges for Earth science, and no one sensor can “do it all”. While many remote sensing technologies are available at present, scientific disciplines are often trained to use only a specific subset, greatly limiting scientific advancements. Here we present open‐source software (icepyx) that lowers the barrier for entry for two remote platforms offering vertically‐resolved information about the ocean's subsurface: ICESat‐2 (Ice, Cloud, and land Elevation Satellite 2) and Argo floats. icepyx provides object‐oriented code for querying and downloading ICESat‐2 and Argo data within a single analysis workflow. icepyx natively handles ICESat‐2 data access and read‐in; here we introduce the Query, Unify, Explore SpatioTemporal (QUEST) module as a framework for adapting icepyx to easily access and ingest other datasets and present Argo data as the initial use case. Seamless retrieval of coincident data from ICESat‐2 and Argo enables improved targeted and exploratory studies across the cryosphere and open ocean realms. We close with recommendations for future work, discussion of the value of open science, relevance of our work to upcoming satellite missions, and an invitation to join our programming community. Link to repository: https://github.com/icesat2py/icepyx/tree/main . Link to documentation: https://icepyx.readthedocs.io/en/latest/ .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it