Characterizing forest vegetation of the Tanana Valley: what can forest inventory and analysis deliver?
Bibliographic record
Abstract
Vegetation profile data were collected as part of a forest inventory project in the Tanana Valley in interior Alaska, providing a means of characterizing the forest vegetation. The black spruce forest type was most common, followed by Alaska paper birch, and white spruce, quaking aspen, and balsam poplar. For individual tree species, black spruce was recorded on 68 percent of all plots, birch was recorded on 67 percent and white spruce on 58 percent. The distribution of growth habits in horizontal layers varied by forest type. There was a higher percentage tree cover in hardwood forest types. Shrubs were prominent in all forest types, dominating in the lowest horizontal layer in black spruce forests and mid layers in other forest types. The most common species recorded include (in descending order) lingonberry, black spruce, Alaska paper birch, bog Labrador tea, white spruce, green alder, bog blueberry, and prickly rose all recorded on at least 35 percent of all plots. A full census of vascular plants on 25 subplots accumulated almost 2.5 times as many species as the Vegetation profile protocol on 101 subplots on the same set of plots.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".