Aggregated herbaceous layer projective vegetation cover at 32 sites in Northwestern Canada, in Summer 2022 (CA-Land_2022_NWCanada)
Bibliographic record
Abstract
Vegetation surveys were carried out in Northwest Territories, Yukon and British Columbia in Canada. The study area is within the boreal forest biome and is partially underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the CA-Land_2022_NWCanada field campaign in July and August 2022. The ground vegetation projective cover in percent was assessed within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were surveyed separately. The assignment of a vegetation type is always unique to a site. Up to four quadrats of 2x2 m were surveyed per vegetation type and projective cover in percent recorded separately for herbaceous and moss layers. All vegetation smaller than 40cm was recorded. Additionally, ground vegetation projective cover was surveyed in 4 rings of 50 cm width around the center of the circular forest plot. Average projective cover per plot was calculated by using an average weighted by vegetation types for each site. The ring survey data was not included in the plot average. In total 32 forest plots were investigated. All data were collected by scientists from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) Germany, and the University of Potsdam Germany.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".