Thresholds for Irrigation Management of Processing Tomatoes Using Soil Moisture Sensors in Southwestern Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Processing tomato (Lycopersicon esculentum Mill.) is an economically important vegetable crop in southwestern Ontario. Processing tomato (cultivar H9553) fruit yield and quality were evaluated in field experiments in southwestern Ontario over a three-year period (2008-2010). A split-plot randomized complete block design with four blocks was used in 2008 and 2010. Irrigation types (buried and surface drip) served as the main plots, while four moisture depletion levels constituted the split plots. In 2009, a 24 factorial complete randomized block design with four blocks was used, with the same two factors. The moisture treatments represented the lower soil moisture triggers, which initiated irrigation scheduling. Irrigation was terminated for each treatment when field capacity was reached. Continuous soil moisture status over the growing season was monitored with a combination of volumetric and tensiometric sensors. Seven fruit quality parameters were monitored: fruit weight, color, pH, size, firmness, Brix yield, and soluble solids. In each year, the most stressed treatment produced the highest soluble solids (6.0, 4.8, and 5.2 Brix for 2008, 2009, and 2010, respectively). Total and marketable fruit yields ranged from 91.9 to 121.1 Mg ha-1 and from 91.4 and 119.7 kg ha-1, respectively. Statistical significance was obtained among treatments and irrigation types in 2008 only. Irrigation water use efficiency was also not statistically significant over the three years. Seasonal irrigation depth ranged from 58 to 196 mm, and statistical significance among the moisture treatments was obtained in 2008 and 2010.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it