Bibliographic record
Abstract
Forest soils often contain many large coarse fragments making it difficult to insert probes to measure soil water content. The ability of time domain reflectometry (TDR) to give reliable measurements of water content in soil with up to 40% coarse fragments was evaluated at a site in the southern interior of British Columbia, Canada. A commercial time domain reflectometer was used with 0.3-, 0.5- and 0.75-m-long probes to measure soil water content of the profile and layers within the profile. A probe had a shorting diode at the surface and two 3-mm-diameter stainless steel rods inserted vertically, 30 mm apart, as the waveguide. Diverging rods or profile discontinuities resulted in erroneous readings that required a review of the recorded signals and recalculation the travel time. Soil physical and hydrologic soil properties were determined and the soil calibrated for TDR. An accuracy of ±0.02 m3m−3 was obtained with measurement of soil bulk density and minimizing probe and travel time errors. Variation in water content between probes reflected the variability in coarse fragment content; however, the ranking of the probes stayed constant with time and rates of change were similar between probes. One standard deviation on the measured change in the volume of water between measurement days for the 0 to 0.5 m depth was ±6 mm (n = 20), equivalent to 0.012 m3m−3. Measurements of water content of the layers had one standard deviation of 0.02 m3m−3. Key words: Time domain reflectometry, forest hydrology, soil water content, water balance
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".