Growth and Yield Variability of Corn (Zea mays), Carrot (Daucus carota), Peas (Pisum sativum) and Potatoes (Solanum tuberosum) Grown in Fallow and Unfallow Standoff Alberta Community Garden Soils
Bibliographic record
Abstract
An experiment was conducted in the Standoff Alberta community garden over the 2019 summer time. Fallow and unfallow soils of Standoff community were used for this experiment. The major nutrients Nitrogen (N) was deficient and Phosphorus (P) was low in the unfallow soil. Furthermore, fallow soil N nutrient was low and optimum for P. Soil potassium was in excess for both soils. The pH of the soils were 7.4 and 7.5 in fallow soil and unfallow soil, respectively. One level of fertilizer application rate was applied to fallow and unfallow soils. Corn, carrots and peas were planted to unfallows soil while potatoes plants were cultivated to fallow soil. Standard agronomic practices were followed to establish this experiment. The six plants were taken per square meter bi-weekly in all the locations randomly across the field in zigzag pattern for growth parameters while six plants for corn, 2 plants for peas, carrots and potatoes per square meter were harvested for yield parameters. The means of growth and yield data collected from each location were subjected to a simple t-test so as to compare the performance of crops planted in each location. The results obtained showed that there were differences of growth in different locations across the field. Moreover, heterogeneous nature of the soil in different locations influenced soil nutrients ability to favour yield of corn, carrots, peas and potatoes. However, in all the 6 locations on the field, peas pod numbers at week 4, potatoes tuber number at week 5, peas dry weight at week 4 and carrot dry weight at week 5 were insignificant, all look the same. These results suggest that application of fertilizers and shortage of water were not evenly distributed which lead to uneven yield in different locations across the field.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".