Bibliographic record
Abstract
The summer of 2024 is shaping up to be one of the most devastating drought years in history. The province’s snowpack, which is the largest source of water in the summer, is on average 63% of normal levels, with some regions like Vancouver Island 46% of normal. Last summer, low rainfall records were set across the province, with many regions recording their driest summers in history. In BC, droughts are expected to get worse as a result of climate change as glaciers retreat, precipitation falls as rain in the winter instead of snow, and summer air temperatures rise. Using publicly available hydrometric and water licence data, we conducted an ecological audit of BC’s Water Sustainability Act to determine if current policies were adequately addressing drought in the semi-arid and highly agricultural Nicola basin. Our analysis determined that since the Act came into force, flows in salmon-bearing streams are consistently below the Critical Environmental Flow Threshold—the minimum amount of flow required to support salmon—during the summer spawning season. High water temperatures were also identified as a frequent occurrence. Our analysis of provincial water licence data also shows that just three larger users account for over half of all water use in the Nicola basin. Provincial water restrictions intended to protect fish populations during drought have only occurred twice in history, in 2009 and 2015, and were significantly delayed in their implementation. We offer eight policy solutions to improve outcomes for salmon during droughts.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".