Snowmelt water use at transpiration onset: Dataset
Bibliographic record
Abstract
This record is for the dataset “Snowmelt water use at transpiration onset: Dataset” at https://doi.org/10.20383/102.0554. Most studies investigate tree water use during the growing season. However, we know little about the source of transpiration during spring onset when trees rehydrate and recommence transpiration. This repository holds high-temporal resolution isotopic (δ18O and δ2H) and hydrometric measurements collected in the spring of 2018 at the Boreal Ecosystem Research and Monitoring Sites (BERMS). Sampling was conducted prior, during and after snowmelt at the Old Black Spruce site (OBS; 53. 98 °N, 105.12 °W) and the Old Jack Pine site (OJP; 53.92°N, 104.69 °W). We did this to characterize tree water use and the timing of transpiration phenological changes from the three dominant tree species – jack pine (Pinus banksiana), black spruce (Picea mariana) and larch (Larix laricina). This composite dataset contains stable isotopic composition data (δ2H and δ18O) of more than 1300 water samples of precipitation, bulk soil collected at different depths in the soil profile, xylem from jack pine, black spruce and larch, and stream from the White Gull Creek. The data set also comprises high-resolution tree hydraulic information from stem radius change and sap flow from all three species. Finally, the repository holds environmental conditions during the sampling period, including soil volumetric water content and temperature at different soil depths, snow depth, air temperature and precipitation. This data was used to understand patterns in tree water use during spring onset and provide a mechanistic understanding of tree water use dynamics by combining isotope hydrology, tree hydrodynamics and phenology. This dataset can be downloaded at https://doi.org/10.20383/102.0554
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.041 |
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".