Tracking water pathways and origins in cranberry production: Isotope hydrology application
Bibliographic record
Abstract
New scientific advances based on integrating water management approaches have been developed in order to reduce the environmental footprint. Cranberry production is one of the most influential cultures in Canada, where water is substantial for irrigation, harvesting, and frost control. The cranberry farms are considered closed-circuits. Water is mainly recycled in large pools, increasing the risk of accumulation of chemical substances affecting the quality of irrigation water. The use of isotopic geochemistry provides an additional layer of information for studying hydrological phenomena in agriculture. The main objective of this project is to identify the sources and sinks of the water in a typical cranberry farm with the help of isotopic hydrology technics and groundwater surveys. Water samples for stable isotope of the water molecule analysis (16O, 17O, 18O, 1H, 2H) were collected during the growing season from May to September (from 2017 to 2020). Preliminary results have shown that isotope hydrology technics can be used to trace the water pathway is a cranberry farm by using the mixing model. These results can help to implement integrated water management procedures helping to increase fruit yields and to reduce environmental impact. Isotope mixing model also makes it possible to assess water losses by infiltration into the aquifers and by evaporation.
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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 | 0.002 |
| 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".