Comparing wetland sampling methods for floristic quality assessment in Superior, Wisconsin
Bibliographic record
Abstract
The Wisconsin Floristic Quality Assessment was utilized as a rapid assessment tool in measuring the \nplant species diversity and species tolerance to anthropogenic disturbance to a subset of wetland \ncommunities in Superior, Wisconsin. Data was collected, analyzed, and compared for two sampling \ntechniques: the Whittaker's Plant Diversity Sampling Method (Shmiva 1984) and the Timed Meander \nSearch Method (Goff et al. 1982) with a goal of identifying a method that best characterizes the \nwetland's floristic quality per wetland community over the amount of time and effort expended. \n \nIn recent years, a Florsitic Quality Assessment for Wisconsin (WFQA) wetlands has been adopted. \nThe FQA originated in an attempt to provide a uniform and repeatable method for assessing natural \narea quality of both uplands and wetlands in the Chicago region (Wilhelm 1977). Following \nrefinement of concepts and methodology (Swink and Wilhelm 1994; Taft et. al. 1997), the use ofFQA \nrapidly expanded. To date, 10 states (Illinois, Missouri, Ohio, Michigan, Wisconsin, Florida, \nNorth and South Dakota, Indiana, and Mississippi) and one Canadian province (southern Ontario) have \nadopted \nthe FQA as a wetland assessment method to complement other functional assessment to9ls. \n' . \n \nThe Wisconsin FQA assessment requires an accurate and complete species inventory of the site based \non homogeneity of community type. The method is based on species conservatism. A numeric value \nfrom 0-10 has been designated by leading experts for each vascular species in Wisconsin and is \ncalled the Coefficient of Conservatism (C of C). These values are averaged into a Mean C and then \nmultiplied against the square root of the total number of native species recorded (BernthaL2003). \n ·· ·
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".