MACROINVERTEBRATE AND BACTERIA COMMUNITY RESPONSES TO TRIBUTARY INPUTS AT COASTAL WETLANDS OF THE DETROIT RIVER, ONTARIO, CANADA
Bibliographic record
Abstract
The Detroit River is a Great Lakes Area of Concern with five monitored wetlands in the Canadian jurisdiction. Habitat assessments have indicated possible stress at wetlands receiving inflow from Turkey Creek and River Canard tributaries. These assessments are made within the tributaries. Yet, macrophyte beds extend into the river upstream and downstream and are important biodiversity habitats. This thesis examines benthic macroinvertebrate and sediment bacteria community compositions for differences with respect to tributaries by two means. First, we examine inter-wetland differences for resemblance to water quality. We had found by NMDS and PERMANOVA that neither taxonomic group resembled water quality index scores. Second, we perform an intra-wetland comparison for Turkey Creek and River Canard to analyze for differences along tributary inputs. Wetland communities were delineated by position into upstream, downstream, and tributary plume strata and analyzed by NMDS and PERMANOVA. Additionally, to detect potentially impaired sample sites and support community differences along tributaries, a multivariate reference approach was applied by dividing sample sites by similar habitat characteristics to contrast River Canard and Turkey Creek to reference wetlands. At neither River Canard nor Turkey Creek we observed significant tributary influence on river communities but had found the Turkey Creek tributary communities significantly differed from the river. Multiple lines of evidence suggest community impairment in Turkey Creek likely from upstream waters rather than tributary inputs.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".