Aqueous geochemistry of the Englishman River Watershed, Parksville, British Columbia for use in assessment of potential surface water-groundwater interaction
Bibliographic record
Abstract
The population in the Englishman River Watershed (ERW) in Parksville, BC is over 50% reliant on groundwater. Increasing development pressures have raised local, provincial and federal government concerns over sustainability of water resources. The Englishman River is viewed as a significant water source to support future growth and economic development. Managing long-term sustainable use of water resources of this watershed is imperative for both ecologic health and economic prosperity. As water demand pressures grow, sustainable water management requires knowledge of the degree of surface water-groundwater (SW/GW) interaction within a watershed. Geochemical methods can provide valuable information on seasonal aquifer contribution and SW/GW interactions. Developing geochemical tools that can place constraints on these complex systems will aid development of hydrogeological models, which can be used to support decision makers in water allocation. This open file provides initial geochemical results from a groundwater well and river sampling program in the Englishman River watershed carried out in 2010-2011.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".