Bibliographic record
Abstract
This presentation provides an overview of contemporary developments in the field of proximal soil sensing (PSS) as it relates to precision agriculture. The ultimate goal of PSS is to enable low-cost acquisition of accurate information on spatial and temporal changes in soil properties across a landscape. Proximal sensing systems rely on gathering signals from transducers placed in contact, or less than 2 m away, from the target. Knowing and understanding the heterogeneity of soil properties helps farmers and other land managers optimize their decision-making process to develop profitable, sustainable, and environmentally friendly operations. The emphasis will be placed on recent efforts in sensor fusion, when sensors that measure different physical phenomena are integrated in a single platform and/or data acquisition process. The recent development of the on-the-spot soil analyser will be used as an example. Under this framework, several sensing methods will be discussed in more detail. These include digital microscopy, soil gas analysis, visible, near-infrared, mid-infrared, gamma-ray as well as laser-induced breakdown spectroscopy, capacitance and apparent electrical conductivity, and ion-selective membranes. Despite their various limitations, these sensing techniques, together with field topography mapping, have been successfully deployed to rapidly determine an array of important physical, chemical and biological soil properties. The recently developed neighbourhood search analysis software has been used to numerically integrate multiple geospatial data layers and produce field areas representing different sensor measurement combinations as well as locating the most informative calibration sites. The presentation will review several alternative approaches to process PSS data to generate thematic soil maps suitable for site-specific management of seed, lime, fertilizer and other soil amendments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".