Bibliographic record
Abstract
Freshwater plays an important role in supporting life on the planet. Agriculture uses 70% of the total accessible freshwater (0.03% of the total global water resources) which makes it important to increase irrigation efficiency so that we can produce more with less freshwater use. SAPs or hydrogels are cross-linked polymeric three-dimensional structures which have the ability to absorb and retain large amounts of water relative to their own mass. The objective of this study was to investigate the gain in water use efficiency and physiological growth of cherry tomatoes (Solanum lycopersicum var. cerasiforme) under different levels of SAP and irrigation treatments. The experiment was laid out in a completely randomised block design with three levels of SAP treatments which include 0% (control), 0.1% and 0.5% and three levels of plant watering application treatments which included everyday watering (control), once in two days watering and once in three days watering from the day of transplant up to four months; all treatments were replicated five times. Trials were carried out in a greenhouse at Macdonald Campus of McGill University in Sainte-Anne-de-Bellevue, Quebec, Canada. The results of the study indicate that 0.5% SAP performed significantly better than control (0% SAP) in terms of yield (P=0.0056) and water use efficiency (P=0.050), whereas, no significant difference was found within the SAP treatments for photosynthetic activity (P=0.5039), stomatal conductance (P=0.5344), transpiration rate (P=0.4907), normalised differential vegetative index (NDVI) (P=0.3901) and plant height (P=0.8967). However, the trends in each of the measurement confirm positive effect of SAP on cherry tomatoes. A microtox toxicology analysis revealed that the SAPs were not toxic to the agro-mix and is safe to use in the fields. Thus, the application of 0.5% SAP can significantly increase the yield and the water use efficiency.
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 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) | 0.001 | 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 teacher head, 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".