Development of the Oklahoma rapid assessment method for floodplain wetlands
Bibliographic record
Abstract
Wetlands provide many important services to society, but degradation of wetlands reduces their ability to provide those services. Loss and degradation of wetlands have been ongoing in Oklahoma since settlement though recent efforts may have begun to reverse some of the damage. To ensure these efforts are working, we need to monitor the ecological condition of wetlands in the state. The Oklahoma Rapid Assessment Method (OKRAM) has been developed as a way to accomplish this goal and has been proven to be an effective tool for measuring the condition of depressional wetlands. OKRAMs intended use is to assess any wetland in the state so it will need to be calibrated for and validated in each wetland type in the state. The goal of this study was to calibrate OKRAM to Riverine Floodplain Wetlands to account for the unique biotic and abiotic conditions within them by altering or changing metrics and/or their scoring. Calibration of OKRAM will serve to prepare it for a statewide validation for Floodplain Wetlands. We performed Level 1, 2, and 3 assessments at 30 wetlands within the North Canadian and Deep Fork River Watersheds and used Level 1 and 3 data to assess Level 2 metrics. Our evaluation showed consistent relationships of OKRAM to Level 1 (e.g., Landscape Development Intensity index) and Level 3 (e.g., Floristic Quality Index) data at 30 floodplain wetland sites within the Deep Fork River and North Canadian River Watersheds of Oklahoma. This study shows that OKRAM can be used as an effective tool to assess floodplain wetlands rapidly and affordably. OKRAM still needs further calibration before I would recommend its use in wetland monitoring programs. I present recommendations for improving poor performing metrics and directions for future research in floodplain wetlands in Oklahoma.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".