Modelling Watersheds as Spatial Object Hierarchies: Structure and Dynamics
Bibliographic record
Abstract
The generation, transport and fate of non‐point source pollutants in surface water systems is recognized as a major threat to water supplies, aquatic and coastal ecosystems. The transformation and movement of water, carbon and nutrients through watersheds integrates a set of ecosystem processes along hydrologic flowpaths. Human individual and institutional interactions with these processes involve direct addition or abstraction of these substances, or the alteration of land cover and drainage systems. In natural and developed catchments, these processes often vary at granularities ranging from below the level of a hillslope, up through regional watersheds. This suggests the need for the development of hierarchical analysis tools that can address the integration of a set of biophysical, biogeochemical and socioeconomic processes over a spectrum of scales. We describe and illustrate the use of a watershed model implemented as a spatial object hierarchy, representing successively contained landform classes associated with class specific processes as member functions. The model has been linked in a range of looser and tighter couplings with GRASS and ArcView, supplemented by specific terrain analytical functions. We illustrate the data and model system for an instrumented catchment monitored as part of the Baltimore Ecosystem Study (BES), a Long Term Ecological Research (LTER) site centering on integrated carbon, water and nutrient cycling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".