Floristic quality assessment for marshes in Alberta's northern prairie and boreal regions
Bibliographic record
Abstract
Floristic quality indices are used to monitor and assess wetland condition by measuring a plant community's tolerance to environmental stress. The aim of our research was to evaluate whether stormwater and reclamation marshes supported wet meadow plant communities that were similar in floristic quality to reference wetlands. Coefficients of conservatism were assigned to a comprehensive list of marsh plant species by nine expert botanists. Various metrics such as the floristic quality index (FQI) were tested for a linear relationship to regional environmental stress gradients in 78 sites in the northern prairies (Aspen Parkland) and 66 sites in the Boreal Plains ecoregions in Alberta, Canada. Sensitivity of floristic quality metrics to the stress gradients was higher when rare species were excluded (species with <5% site-level cover). An adjusted FQI that eliminated bias towards sites with higher species richness yielded the strongest relationship to the stress gradient in both ecoregions (r2 = 0.55 in northern prairies; r2 = 0.46 in Boreal Plains). The adjusted FQI also yielded more consistent scores than richness-weighted metrics in a subset of 47 sites where sampling was replicated in dry and wet years (r = 0.75). Including exotic species in floristic quality metrics was not beneficial in the northern prairies where reference sites were located near areas of high urban and agricultural development. This study demonstrates that floristic quality assessments are reasonably good predictors of plant community condition in relation to environmental stress. Results of this study also highlight that existing stormwater ponds and reclamation marshes are not successfully restoring plant community 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".