Bibliographic record
Abstract
Irrigation is a well established procedure on many farms in western Canada and is practiced on various levels around the world. It allows diversification of crops, while increasing crop yields. However, typical irrigation systems consume a great amount of conventional energy through the use of electric motors and generators powered by fuel. The overall objective of this research was to determine the feasibility of using photovoltaic (PV) modules to power a water pump for a small-scale drip irrigation system in Montreal (Quebec, Canada). The study involved field observations, as well as computer simulations of global solar radiation and PV electrical output. Field observations involved a summer and winter installation of two amorphous silicon 42 W PV modules, directly connected to a 12 V surface water pump. The parameters monitored were voltage, current, back-of-panel temperature, pressure, and flow. These observed parameters were used to determine PV electrical output and volume of water pumped. Site latitude, elevation, and panel tilt were applied to the solar radiation and PV electrical output models, along with the following meteorological data: daily average, maximum, and minimum temperatures, and global solar radiation. Daily solar radiation prediction showed a linear correlation of 0.69 with the observed daily values, over the years 2000 to 2005. The correlation coefficient was improved to 0.91, when 7 day moving averages of both the observed and predicted solar radiation data were used. PV electrical output and volume of water pumped were monitored between August 2005 and May 2006. Both the power and water output observations were less than expected. However, the predicted daily PV electrical output ranged from 1.0 MJ d-1 in the summer to approximately 0.6 MJ d-1 in the winter. As expected, an increase in power caused an increase in the volume of water pumped.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".