MétaCan
Menu
Back to cohort
Record W4403261515 · doi:10.1672/ucrt083-604

Bunchgrass Meadows — Among Our Wetlands of Distinction

2024· article· en· W4403261515 on OpenAlexaboutno aff
Colin MacLaren

Bibliographic record

VenueWetland Science and Practice · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandTussockGeographyEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

In the northeast corner of Washington State sit the Selkirk and Kettle mountain ranges. There, woodland caribou, grizzly bear, and gray wolves freely roam 1.5 million acres of spruce and fir forest, talus, and river valleys. Within this geographic area is Bunchgrass Meadows, a 711-acre (288-hectare) wetland complex within the Colville National Forest that stands out as one of the many gems on our public lands and one of the newest “Wetlands of Distinction”. Bunchgrass Meadows is at the headwaters of Harvey Creek and at the crossroads of Kootenai, Colville, Kalispel, Spokane, and Okanogan traditional tribal lands (Figure 1). This relatively high-elevation feature (about 5,084 ft., 1,550 m) is found in the Canadian Rocky Mountain ecoregion near the boundary between Washington and Idaho. Snowmelt and rain confined by the surrounding hillslopes collect and linger on a slow meander to Harvey Creek at the northeastern end, sustaining an exceptionally rich and diverse array of wetland habitats (riverine, lacustrine, slope, and depressional; Figure 2). Multiple natural resource management agencies have compiled documentation on the unique structure and composition of this wetland complex. The U.S. Forest Service (USFS) designated Bunchgrass Meadows as a Research Natural Area (RNA) in 2008. The USFS’ regional RNA Committee found Bunchgrass Meadows to be exemplary of terrestrial and wetland ecosystems, including high elevation mountain meadow,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.270
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueWetland Science and PracticeSame topicRangeland and Wildlife ManagementFrench-language works237,207