Boreal tree water deficit and environmental variables across five sites in western Canada
Bibliographic record
Abstract
The study incorporates a dataset gathered from five forest stands (Old Black Spruce, Scotty Creek, Baker Creek, Smith Creek, and Havikpak Creek) in Canada's western boreal forest. The data encompasses multiple environmental variables critical for understanding tree water dynamics. The dataset includes tree water deficit (TWD) measurements derived from continuous measurements of stem radius change from black spruce (Picea mariana) and tamarack (Larix laricina). Automatic circumference band dendrometers (model DC-2 and 3, Ecomatik, Munich, Germany) were used to obtain 30-minute measurements of stem radius change and data from up to four dendrometers were recorded on a single HOBO data logger (UX120-006M, Bourne, MA). Concurrently, environmental controls including photosynthetically active radiation, air temperature, vapour pressure deficit, rainfall, and soil moisture were measured continuously every half hour on micrometeorological towers located near the instrumented trees. As were gas analyzer-based or hygrometer-based eddy covariance measurements of stand-level evapotranspiration. These parameters collectively enable an analysis of the environmental controls on TWD across various temporal scales. Data was obtained for the peak growing season (June 1st to August 31st) in 2018, 2019 and 2020. Measurements began between June 6 - 15, 2018 at SCC, BAC, SMC, and HPC. Photosynthetically active radiation was not available for BAC in 2018, 2019 and 2020, and soil moisture data was missing for BAC in 2020. The primary goal of data collection was to understand the intricate interactions between environmental variables and tree water stress, contributing to a broader understanding of how boreal forests respond to climate change. The dataset allows for advanced analytical techniques, including wavelet analysis and Granger causality, to unravel complex relationships and causalities between TWD and potential environmental controls.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".