Agricultural hydroinformatics: agricultural water systems management as a new application for hydroinformatics
Bibliographic record
Abstract
<p>Hydroinformatics, throughout its more than 25 years of existence, has been applied to a set of research areas. So far, these applications include: hydraulics and hydrology, environmental science and technology, knowledge systems and knowledge management, urban water systems management.</p><p>This paper introduces agricultural water systems management as a new application for hydroinformatics, and terms it as “agricultural hydroinformatics”. It presents a discipline-delineated conceptual framework originating from the particularities of the socio-technical dimension of applying hydroinformatics in agriculture. It epitomizes the wholeness and inter-dependencies of agricultural systems studies and modelling. It is suitable to support, not only integrated agricultural water resources management in particular, but also agricultural sustainability in general, in addition to a wide range of agricultural development situations beyond connections between agro-economic and water engineering development and its socio-economic impacts.</p><p>The paper also highlights some contributions of hydroinformatics to agriculture including new kinds of sensing technologies, information and simulation models development that bear the potential to boost reproducibility of agricultural systems research through systematic and formal records of the relationships among raw data, the processes that produce results and the results themselves.</p>
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".