2011 Fall Soil Moisture Survey
Bibliographic record
Abstract
Each sampling site was chosen based on the soil properties that best represent each area and the most common cropping system in the region. Soil samples were collected from 101 locations at five depths throughout the root zone: 0-15 cm, 15-30 cm, 30-60 cm, 60-90 cm, and 90-120 cm. Samples were placed in sealed containers and subsequently weighed, oven dried, and re-weighed to determine their gravimetric moisture content. Soil samples were classified based on their similarities to other well characterized soils in Manitoba according to Haluschak et al (2004). This enabled us to assign a bulk density, wilting point, field capacity, and available water holding capacity value to each depth from each sample location. From there, soil moisture by weight was converted to percent moisture by volume. Then available water and soil moisture as a percent of available water holding capacity were calculated and mapped. An inverse distance weighted (IDW) interpolation technique with minimal smoothing was used to retain the spatial variability of the results. The interpolation was performed between the actual values of the sample sites without accounting for soil variability between those locations.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".