Positioning Temperature Sensors for Frost Protection in Northern Cranberry Production
Bibliographic record
Abstract
Frost can cause serious economic losses in cranberry fields, particularly in northern regions. When the air temperature reaches a low critical threshold, sprinklers are operated to protect vines, to insure crop production and profitability. To avoid frost injury, proper positioning of temperature sensors is critical. A field experiment was designed and conducted to determine the optimal installation height of sensors above soil surface. Temperature data was used to investigate the spatial temperature gradient in the section of a cranberry field. A computer simulation of the temperature profile was performed to simulate the effect of wind velocity on the prediction of air temperature. For optimal use, sensors should be installed at the height of the canopy and several meters away from a dike. On nights with low wind velocities, the canopy air temperature was 2.7°C below that of 500 cm above the ground. The sensors should be put at least five m away from a dike to avoid the transfer of heat from the dike to the sensor. Also, multiple sensors should be installed because of the large variations in air temperature that were measured across the experiment. The simulated temperature indicated that wind velocity strongly influenced the temperature estimation; the effect of the wind on temperatures gradients was greater when the wind velocity was low (<2.3 m/s).
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".