Amongst the living: Willows prove they belong in group of snow fences
Bibliographic record
Abstract
While farm country can seem peaceful in the winter months, blanketed by snow, these landscapes can also create hazardous situations for road drivers, as strong winds can blow snow onto roadways. Reduced visibility, ice roads, increases in travel time, snow-removal costs, and road salt applications can all result from blown snow. That is why preventive methods to control blowing and drifting snow are crucial, and one such method is the use of living snow fences (LSF). These are windbreaks of trees, shrubs or grasses that are planted in order to keep snow and ice from blowing off fields onto adjacent roads. The windbreaks are usually required to be set back a certain distance from the roadway in order to work properly, with wind turbulence forming deposits of snow drifts around them; however state-owned rights-of-way are often not wide enough to accommodate these LSFs. In Minnesota, shrub-willows have been identified as a native plant (native to much of the U.S. and Canada) that is already seen in many roadside ditches, and they have been extensively researched as a potential biomass crop for bioenergy. Many of the same characteristics that make willows ideal for biomass, including their fast and abundant growth, also make them ideal for LSFs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".