Bibliographic record
Abstract
This study investigated the possibility of three approaches in determination of soil moisture in the root zone. The aim of the study was to contribute to the development of soil moisture monitoring methods to better help crop best management practices. Two fields were examined, one at the Macdonald Campus of McGill University and the other near St. Jean-sur-Richelieau. Three approaches were used; (1) a hand-held hyper-spectral sensor (350-2500 nm), (2) a Geonics RTM EM-38 conductivity meter and, (3) gravimetric soil moisture sampling. The first experiment (at St. Jean-sur-Richelieu) investigated the possibility of monitoring soil moisture with the EM_38, in the presence of field elevation and soil texture. The second experiment (at Macdonald Campus) investigated the possibility of using hyper-spectral sensor data for determination of soil characteristics in the root zone, in the presence of such factors as (a) irrigation (main treatment), (b) nitrogen (sub-treatment), and (c) weed control (sub-sub-treatment). Statistical regression analyses and Artificial Neural Network models were used to select the best waveband region for determination of soil root zone moisture. The coefficients of determination obtained by the statistical analyses ranged from 0.75 to 0.94. The wavebands most frequently identified by these analyses ranged from 1100 nm-1900 nm. The performances of the ANN training models were considered acceptable (R2 from 0.6 to 0.8). The lack of sufficient data greatly impacts this approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".