Examining climate change & irrigation requirements on James Island, British Columbia
Bibliographic record
Abstract
As the Earth’s climate continues to change, so too does the availability of\nfreshwater resources. Small islands are at the forefront of freshwater vulnerability\nand therefore must be ready to adapt. In order to implement effective adaptation\nmeasures, accurate projections of future water use are required. This study\nexamines the climate and water use of James Island, British Columbia, during the\nfive-year period 2009–2013. Potential future climate scenarios for James Island,\ncreated using the guidelines set forth by the United Nations Intergovernmental\nPanel on Climate Change 5th Assessment Report are also examined. The future\nclimate projections, along with the climate and irrigation data, are utilized to create\na new method for estimating potential future irrigation requirements based on\nirrigation flow per growing degree-days. Future increase in annual mean\ntemperature of 0.9°C for the 2020s, 1.9°C in the 2050s and 2.8°C in the 2080s\nsuggest an increase in irrigation requirements of 17% in the 2020s, 38.5% in the\n2050s and 59% in the 2080s. In conclusion, the merits of alternative irrigation\nstrategies such as water reclamation and desalination are discussed, as well as\ntheir potential application for James Island.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".