Hydrologic assessment of a small maritime hydrometric monitoring network using a combined inductive-deductive process-informed catchment classification system
Bibliographic record
Abstract
The National Hydrometric Program, operated by the Water Survey of Canada, is the primary source of surface water quantity data in Canada. The network is cost-shared between the federal and provincial governments, and decisions relating to station placement are made according to both federal and provincial interests. Nova Scotia is a small maritime province in Atlantic Canada that is roughly one third the size of England and Wales and has a diverse climate and geology. The Nova Scotia hydrometric monitoring network currently consists of just 31 stations. The overall objective of this study was to determine how well the current network captures the level of hydrologic variability expected in the province using a regional catchment classification scheme. To accomplish this, we developed a combined inductive-deductive catchment classification system and applied it to the province’s active monitoring network and ungauged major watersheds. Initially, hydrologic signatures were used to quantify the catchment function of 47 long-term gauged catchments and to cluster similarly behaving catchments. We identified five generalized flow classes and then attempted to replicate this classification using a deductive-based decision tree framework with physiographic and meteorological explanatory variables. The validated decision tree was used to classify the active hydrometric network and 250+ major watersheds in the province. The network was assessed to determine how well it covered the expected hydrologic variability in the major watersheds across the province. The decision tree proved to be a useful tool for understanding the current network’s coverage and could also be easily applied by practitioners to identify appropriate donor catchments for ungauged watersheds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".