Phytomass patterns across a temperature gradient of the North American arctic tundra
Bibliographic record
Abstract
Only a few studies to date have collectively examined the vegetation biomass and production of arctic tundra ecosystems and their relationships to broadly ranging climate variables. An additional complicating factor for studying vegetation of arctic tundra is the high spatial variability associated with small patterned‐ground features, resulting from intense freeze‐thaw processes. In this study, we sampled and analyzed the aboveground plant biomass components of patterned‐ground ecosystems in the Arctic of northern Alaska and Canada along an 1800‐km north‐south gradient that spans approximately 11°C of mean July temperatures. Vegetation biomass was analyzed as functions of the summer warmth index (SWI–sum of mean monthly temperatures > 0°C). The total absolute biomass (g m−2) and biomass of shrubs increased monotonically with SWI, however, biomass of nonvascular species (mosses and lichens), were a parabolic function of SWI, with greatest values at the ends of the gradient. The components of plant biomass on patterned‐ground features (i.e., on nonsorted circles or within small polygons) were constrained to a greater degree with colder climate than undisturbed tundra, likely due to the effect of frost heave disturbances on the vegetation. There were also clear differences in the relative abundances of vascular versus nonvascular plants on and off patterned‐ground features along the SWI gradient. The spatial patterns of biomass differ among plant functional groups and suggest that plant community responses to temperature, and land‐surface processes that produce patterned‐ground features, are quite complex.
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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 | 0.001 |
| 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) | 0.001 | 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".