Software to Enable Ocean Discoveries: A Case Study With <scp>ICESat</scp>‐2 and Argo
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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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".