Using a Geospatial Model to Relate Fluvial Geomorphology to Macroinvertebrate Habitat in a Prairie River—Part 2: Matching Family-Level Indices to Geomorphological Response Units (GRUs)
Bibliographic record
Abstract
Many rivers are intensely managed due to anthropogenic influences such as dams, channelization, and water provision for municipalities, agriculture, and industry. With this growing pressure on fluvial systems comes a greater need to evaluate the state of their ecosystems. The purpose of this research is to use a geospatial model of the Qu’Appelle River in Saskatchewan to distinguish instream macroinvertebrate habitats at the family level. River geomorphology was assessed through the use of ArcGIS and digital elevation models; with these tools, the sinuosity, slope, fractal dimension, and stream width of the river were processed. Subsequently, Principal Component Analysis, a clustering technique, revealed areas with similar sets of geomorphological characteristics. These similar typology sequences were then grouped into geomorphological response units (GRUs), designated a color, and mapped into a geospatial model. Macroinvertebrate data was then incorporated to reveal several relationships to the model. For instance, certain GRUs contained more highly sensitive species and healthier diversity levels than others. Future possibilities for expanding on this project include incorporating stable isotope data to evaluate the food-web structure within the river basin. Although GRUs have been very successful in identifying fish habitats in other studies, the macroinvertebrates may be too sessile and their habitat too localized to be identified by such large river units. Units may need to be much shorter (250 m) to better identify macroinvertebrate habitat.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".