Distribution and Biomass Allocation in Relation to Depth of Flowering Rush (<i>Butomus umbellatus</i>) in the Detroit Lakes, Minnesota
Bibliographic record
Abstract
The Detroit Lakes chain of lakes consists of five basins in northwest Minnesota adjacent to the town of Detroit Lakes. Flowering rush has been established in these basins since the 1960s. We evaluated the distribution of flowering rush in the five basins using a point intercept method, with 830 points distributed in a grid with points 150 m apart. These data were analyzed to determine whether invasive and native species frequencies were different between 2010 and 2011. We also assessed co-occurrence of flowering rush with native hardstem bulrush. The distribution of both flowering rush and hardstem bulrush was unchanged from 2010 to 2011. Flowering rush is invading areas with native plants and not establishing in unvegetated areas. Although flowering rush is found as deep as 4.5 m, it is most frequent at a depth of 1.3 m. We also examined the distribution of biomass and growth across a depth gradient from 0.3 to 3.0 m in 0.3-m intervals. At each 0.3-m interval, three biomass samples were collected at each of 10 transects for a total of 30 samples per depth interval or 300 biomass samples. At each point, leaf height, emergent leaf height, water depth, number of ramets, and number of rhizome buds were counted. Biomass samples were collected in a 0.018-m2 core sampler, sorted to shoots and belowground biomass. We found that flowering rush height and biomass peaked at 1.3 m and declined with greater depth. Bud density was negatively related to water depth. Bud density averaged 300 buds m–2, which was three times the average ramet density (100 ramets m–2).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".