Range plant community types and carrying capacity for the subalpine and alpine subregions : second approximation /
Bibliographic record
Abstract
How to use guide 4 Results 26 Subalpine (Grassland and Shrubland ecology) 27 Central and Northern Foothills areas 28 Ecology of ecodistricts 29 Key to grasslands in the Foothills 33 Key to shrublands in the Foothills 34 Community types Grasslands SACFA1 .Water sedge-Beaked sedge meadows 35 SACFA2.Tufted bulrush 36 SACFA3.Sedge-Cottongrass 37 SACFA4.Tufted hairgrass-Sedge 38 SACFA5.Sedge-Tufted hairgrass 39 SACFA6.Sedge-Rocky Mtn.fescue-Alpine timothy 40 SACFA7.Sedge-Slender wheatgrass-Fringed brome/Forb 41 SACFA8.California oatgrass-Sedge 42 SACFA9.Rough fescue-Hairy wildrye-Sedge 43 SACFA10.Sedge-Hairy wildrye 44 SACFA1 1 .Blunt sedge-Junegrass/Bearberry 45 SACFA12.Fringed sage/Sedge-Junegrass 46 SACFA13.Sedge-Bog sedge-Tufted hairgrass 47 SACFA14.White Mtn.avens/Bog sedge 48 SACFA15.Creeping red fescue-Sedge 49 S ACFA1 6. Kentucky bluegrass-Sedge/Dandelion 50 SACFA1 7. Fireweed-Meadow rue/Sedge-Hairy wildrye 5 1 Shrublands SACFB 1 .Willow-Bog birch/Water sedge 52 SACFB2.Willow/Horsetail 53 iii SACFB3.Willow/Graceful sedge SACFB4.Willow-Bog birch/Tufted hairgrass SACFB5.Willow-Bog birch/Clover-Dandelion SACFB6.Willow-Bog birch/California oatgrass SACFB7.Willow-Bog birch/Hairy wildrye SACFB8.Bog birch/Bog sedge-Sedge SACFB9.Bog birch-Willow/Rough fescue S ACFB 1 0. Bog birch/Rough fescue-Bog sedge 6 1 SACFB 1 1 .Willow/Fringed brome-Sedge Central and Northern Rocky Mountain areas Ecology of ecodistricts Key to the grasslands within the Mountain ecodistricts Key to the shrublands within the Mountain ecodistricts Community types Grasslands SACMA1.Bog sedge-California oatgrass
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.012 | 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".