Using a Geospatial Model to Relate Fluvial Geomorphology to Macroinvertebrate Habitat in a Prairie River—Part 1: Genus-Level Relationships with Geomorphic Typologies
Bibliographic record
Abstract
Modern river ecosystems undergo constant stress from disturbances such as bank stabilization, channelization, dams, and municipal, agricultural, and industrial water use. As these anthropogenic water requirements persist, more efficient methods of characterizing river reaches are essential. Benthic macroinvertebrates are helpful when evaluating fluvial health, because they are often the first group to react to contaminants that can then be transferred through them to other trophic levels. Hence, the purpose of this research is to use a geospatial model to differentiate instream macroinvertebrate habitats, and determine if the model is a viable method for stream evaluation. Through the use of ArcGIS and digital elevation models, the fluvial geomorphology of the Qu’Appelle River in Saskatchewan (SK) was assessed. Four geomorphological characteristics of the river were isolated (sinuosity, slope, fractal dimension, and stream width) and clustered through Principle Component Analysis (PCA), yielding sets of river reaches with similar geomorphological characteristics, called typologies. These typologies were mapped to form a geospatial model of the river. Macroinvertebrate data were aligned to the locations of the typologies, revealing several relationships with the fluvial geomorphology. A Kruskal-Wallis analysis and post hoc pairwise multiple comparisons were completed with the macroinvertebrate data to pinpoint significant genera, as related to the geospatial model.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".