The Physiology of Betula glandusa on Two Sunny Summer Days in the Arctic and Linkages with Optical Imagery
Bibliographic record
Abstract
Controls on Arctic vegetation physiology have been linked to microscale (1–100 m) topography and landscape position, yet drivers may change under future climates as temperature, active-layer thickness, and nutrient limitations are removed or altered. Focusing on the cosmopolitan dwarf birch (Betula glandusa), physiological metrics were measured over two field campaigns at Trail Valley Creek, NWT, Canada, and linked to tasked and archived multispectral imagery to investigate drivers. Relative humidity was ~31.1% on 25 June 2023, and increased to 45.6% on 29 June 2023, which corresponded to heightened physiological activity of stomatal conductance and light-adapted fluorescence (gsm: 0.118 vs. 0.165 μmol m−2 s−1, Fs: 129.29 vs. 178.42). Normalized difference vegetation index of AVIRIS, Sentinel 2, and SkySat were negligibly correlated to dwarf birch physiological activity, but moderately correlated to dwarf birch height and active-layer thickness. Random forest variable importance revealed that environmental factors and field-measured active-layer thickness ranked higher than remote sensing metrics in explaining physiological activity regardless of the field campaign. Overall, these findings suggest that microscale variation can influence dwarf birch physiological activity, yet microscale effects are overwritten by environmental conditions that may hinder fine-scale space-based monitoring of Arctic vegetation physiological dynamics.
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 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 | 0.000 |
| 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) | 0.000 | 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".