Winegrape cold hardiness in Okanagan Valley vineyards between 2012 and 2019
Bibliographic record
Abstract
Understanding how plants' physiological tolerances vary between genotypes is important for understanding current habitat suitability and potential range shifts with climate change. In this dataset we focus on cold hardiness of different genotypes of Vitis vinifera subsp. vinifera plants in the Okanagan Valley, BC, Canada. We collected data from 18 different genotypes representing 90% of the study region's harvest. Samples came from 13 different vineyard sites in the Okanagan Valley, wich is located at the northern range edge for the species (latitudes 40.0 to 50.5). Buds from 3 to 6 vines were collected biweekly from late October to early April every year between 2012 and 2019. We measured buds from nodes 3 through 7. We then took a mean of all the buds of all the vines for a given site and genotype. We used differential thermal analysis (DTA) to estimate the cold hardiness of each bud. This method detects heat spikes released when internal cellular water freezes, which is the point where the buddies. The final bud cold hardiness values used in our model are the mean of 15 buds per genotype and location, and are expressed as the Low Temperature Exotherms where there was 50% bud mortality(LTE50), the standard method for estimating critical lethal temperatures for winegrape field damage.We include the mean of the minimum and maximum daily temperature at the Penticton weather station on the day the material was collected. original weather data can also be downloaded directly from https://climate.weather.gc.ca/historical_data/search_historic_data_e.html?Month=10&Day=11&Year=2023&timeframe=2&StartYear=1840&EndYear=2023. We first used this data in the manuscript in The role of genotypic and climatic variation at the range edge: A case study in winegrapes by Faith A M Jones, Carl Bogdanoff and E M Wolkovich, accepted in the American Journal of Botany in October 202
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".