Relative influence of landform, hydrology, and stream habitat on benthic macroinvertebrate communities in a managed northern hardwood forest
Bibliographic record
Abstract
In this study, I use a combination of field measurements and modelling based on a comprehensive suite of landscape, hydrological, chemical, and habitat variables to develop predictive relationships between these variables and benthic macroinvertebrate (BMI) community composition of forested streams, which can be used as indicators of Aquatic Ecosystem Services (AES). I focus on the Lower Batchawana Watershed (LBW), a mixed hardwood forest north of Sault Saint Marie, Ontario, which contains a gradient of disturbance, ranging from undisturbed to intensively harvested over the past 25 years. I show that catchment size and stream flow rise rate have the largest influence on BMI community structure in the LBW, while forest harvest had no measurable effect. By better defining the relationship between physicochemical and biological indicators of AES, I hope to provide forest managers with the information required to make effective monitoring and management decisions aimed at ensuring sustainability of forest-based AES.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".