Formation and evolution of melt holes on vine-shoots during the winter in Quebec
Bibliographic record
Abstract
Vine cultivation in Quebec is confronted with the problem of an unfavorable climate specially because of winter frost. Over the last few years, artificial snow has been used, at the experimental level, as one of the protective methods implemented during winter at the Sous les Channilles vineyard, in southern Quebec. However, towards the end of January, the increase in solar radiation intensity is instrumental in causing the fonnation of inelt holes along the vine shoots thus reducing the protective effect of the snow cover. According to varying climatic conditions, we show that there are many factors at the origin of the formation and the metamorphoses of melt holes. These melt holes can be fonned by sublimation or fusion of the snow and can sometimes present mixed fonns. Through their presence, they modify the vertical temperature gradients in the snow cover and play a primary rôle in the evolution of snow metamorphoses near the stocks. The formation of melt holes near the vine shoots can also damage the fruit buds submitted to cold temperatures prevailing on the surface. The empty space created near the stocks can permit the infiltration of cold air and, in situations of extreme cold, damages can be sustained by the fruit buds. On the other hand, our observations also show that, when melt holes are covered by snow, a constructive metamorphosis is initiated and the melt holes can fill themselves up through the evolution of internal frost. If the melt holes are not too large, the formation of frost can obstruct the surface orifice and restrict the infiltration of cold air from the exterior within the snow cover. Furthermore, the widening of melt holes early in the spring can cause the premature disappearance of the snow cover in the vine rows and, accordingly, can expose the buds to congelifraction temperatures below the cryotolerance threshold. Artificial snow is more efficient than natural snow in the sense that, since its volumetric mass is normally superior to that of natural snow, the development of melt holes is a lot slower. Accordingly, artificial snow provides more durable protection in the spring. The study of the pro¬ cesses controlling the formation and the evolution of melt holes has also shown the large variability of the snow cover according to local meteorological conditions. Finally, the understanding of the processes at the base of the formation of melt holes sheds new light on the rôle of snow in agriculture.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".