Monitoring Shallow Vadose Zone Moisture Dynamics using Electrical Resistivity Tomography and Electromagnetic Induction
Bibliographic record
Abstract
This hydrogeophysical study examines the capacity of the surface electrical resistivity tomography (ERT) and electromagnetic induction (EMI) methods to monitor soil moisture dynamics in the shallow vadose zone over the range of conditions encountered during multiple annual cycles. \n \nHigh-resolution ERT and EMI surveys were performed to monitor changes in shallow moisture conditions at a vineyard located in Vineland, Ontario, Canada. Twenty-five metre profile lines were established at five monitoring sites with soil textures ranging from silty clay to silt loam. ERT surveys were performed at each of these five sites whereas EMI data were acquired at only three of these monitoring sites due to the presence of wire trellises at the other sites. Geophysical surveys were performed approximately every two to four weeks. The geophysical data set is supplemented by precipitation and temperature observations, as well as historic soil temperature readings to 3 meters depth, taken at a nearby weather station and gravimetric water content measurements obtained at each monitoring site. The combination of the multi-year monitoring period, dense temporal sampling interval and concurrent use of both the ERT and EMI methods, as well as the supplemental weather and gravimetric information make this a very unique hydrogeophysical data set. \n \nA wide range of soil moisture conditions were encountered at the monitoring sites including wet spring and fall, dry summer, and frozen winter periods, as well as contrasting conditions between the two annual cycles (e.g., relatively wetter versus drier summer conditions). Temporal variations observed in both the ERT and EMI data qualitatively agrees well with shallow soil moisture conditions (i.e., the upper most 0.50 meter) inferred from the gravimetric measurements and weather data. In addition, ERT results for the subsurface below 1 meter appear to follow the historical pattern of soil temperature. In addition, there is very good qualitative agreement between the ERT and EMI data set in terms of their temporal and spatial variability. However, a quantitative analysis of the relationships between gravimetric data, soil temperature and geophysical data reveals that additional work is needed to understand the nature of these relationships. Further, a basic quantitative comparison of the ERT and EMI results reveals divergences that require more investigation.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".