<i>Development of an integrated sensor system for automated on-the-spot measurement of physical soil properties</i>
Bibliographic record
Abstract
Abstract. Advancements in soil sensor technology have allowed for comprehensive measurements of soil physical characteristics. Sensor measurements help in understanding the qualities of the soil rooting zone; they can be used to create detailed soil maps to facilitate the application of site-specific management decisions for agriculture or resource management purposes. Currently, the benefits and widespread use of these advanced methods are hindered by several factors, including the ability to capture several soil properties at once, consistency between measurements, and the labour required to collect the data. This paper describes a partnership between the autonomous electric tractor company Ztractor and McGill University, for the development of a sensor system capable of capturing several soil physical characteristics at one time through on-the-spot measurements linked to the autonomous tractor Bearcub. Integrating traditional measurement techniques with automated functionality, the sensor platform, centered around a cone penetrometer, logs data for several soil physical properties to the tractor. The automated functionality seeks to improve the quality of data as all parameters in the testing process are standardized, eliminating inconsistencies caused by manual measurements.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.022 | 0.008 |
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".