Laurentian Great Lakes integrated hydrologic model data package
Bibliographic record
Abstract
Modelling groundwater-surface water (GW-SW) interactions at scales of large river basins is a difficult challenge. Xu et al (2021) have completed such a modelling exercise for the 766,000 km2 Laurentian Great Lakes basin using a HydroGeoSphere (HGS) fully-integrated surface watergroundwater model accounting for hydrologic seasonality under monthly normal climatology. Many datasets are developed on a national basis, and development of the underlying HGS model data required synthesis of both Canadian and United States data sets. Most of the datasets are in the public domain; however; considerable effort is required to standardize the respective data. The objective of this Open File data release is to make the underlying data layers and the finite element mesh available for use by anyone interested. The data release consists of 16 folders of data layers, and attribute information for aspects of the HGS modelling platform inputs. Not all layers may be necessary to run the model in alternative groundwater modelling software. Details on the model results are available in Xu et al (2021).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.028 |
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".