Commemorating the 50th anniversary of the Freeze and Harlan (1969) Blueprint for a physically-based, digitally-simulated hydrologic response model
Bibliographic record
Abstract
The year 2019 marked the 50th anniversary of a pioneering publication in hydrology. Allan Freeze and Richard Harlan published their Blueprint for a physically-based, digitally-simulated hydrologic response model (Freeze and Harlan, 1969) in Journal of Hydrology. Their vision was for a futuristic model that would integrate key processes and compartments in the hydrologic cycle: precipitation, evapotranspiration, overland runoff, infiltration and groundwater exchange (into and out of) surface water bodies, such as rivers and lakes. Today, the original Blueprint is a reality. We recently published a paper in Journal of Hydrology to commemorate the 50 year anniversary of the original Blueprint paper (Simmons et al., 2019). In this talk, we present an overview of, and highlights from, this paper. Through personal communications with Allan Freeze, we present the history and genesis of the Blueprint paper. We reflect on the uptake of the Blueprint into modern hydrology, the development of numerical models that enabled this, and the range of challenges being tackled by these models. Finally, we consider challenges and opportunities for the future of this area of modelling and hydrologic science. Reference Simmons, C.T., Brunner, P., Therrien, R., and Sudicky, E.A., 2019. Commemorating the 50th anniversary of the Freeze and Harlan (1969) Blueprint for a physically-based, digitally-simulated hydrologic response model, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2019.124309
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".