Hydrological Perspectives on Integrated, Coordinated, Open, Net- worked (ICON) Science
Bibliographic record
Abstract
This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 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".