Bibliographic record
Abstract
In 1996, the Bureau of Land Management and US Forest Service, in partnership with the Natural Resources Conservation Service, created a national riparian strategy called “Accelerating Cooperative Riparian Restoration and Management.” An interagency, interdisciplinary team, the National Riparian Service Team (RNST), based in Prineville, Oregon, was established to implement the Strategy. To assist with implementation of the Strategy, a Riparian Coordination Network (RCN) has been established with Riparian Service Teams in each of the western states, Canada, and Mexico. The RNST and RCN has adopted as foundational tools, the Proper Functioning Condition (PFC) riparian assessment protocol, a methodology for assessing the functionality and health of riparian areas, and the Multiple Indicator Monitoring (MIM) protocol, a methodology for monitoring use and management impacts on stream channels and riparian vegetation. The PFC methodology provides a consistent approach for assessing the physical functioning of riparian areas through consideration of hydrology, vegetation, soil and landform attributes. MIM is a monitoring methodology that allows for statistical analysis of a comprehensive group of interrelated indicators, including three short-term and seven long-term indicators. This presentation gives a brief introduction to these two useful tools for assessing and monitoring riparian areas. Training opportunities provided by national and state Riparian Service Teams to help practitioners become proficient in the proper use of these tools are also mentioned.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".