Soil moisture, vegetation analysis and raw logger research dataset: Elora Research Station, Elora, Ontario [Canada] May 19, 2015 to September 16, 2016
Bibliographic record
Abstract
Aaron Berg's Research Group in the Department of Geography, College of Social and Applied Human Sciences, University of Guelph, collects information from five different agricultural fields at the Elora Research Station. The purpose of the study is to evaluate the effect of increased vegetation on backscatter measurements as collected from the RADARSAT-2 satellite. This dataset contains backscatter, soil moisture, dielectric, vegetation water content (W), and leaf area index (LAI) measurements. The POGO probe collects soil moisture and dielectric constant measurements from various sites and depths on each field. The RADARSAT-2 satellite collects backscatter (dB) which can be derived to estimate soil moisture, leaf area index, and volumetric water content measurements. The LAI-2200C Plant Canopy Analyzer was used to measure leaf area index. The study aims to determine the point at with the RADARSAT-2 satellite loses sensitivity in backscatter as a result of increased vegetation growth. This is analyzed by comparing the strength of the relationship between RADARSAT-2 backscatter and field based soil moisture, LAI, and W during vegetation development. Then, using piecewise regression before and after the derived inflection point. This set includes data from May 19th, 2015 to September 16th, 2016.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".