The Kwakshua Watersheds Observatory, central coast of British Columbia, Canada
Bibliographic record
Abstract
Abstract The Kwakshua Watersheds Observatory (KWO) is an integrative watersheds observatory on the coastal margin of a rain‐dominated bog‐forest landscape in British Columbia (BC), Canada. Established in 2013, the goal of the KWO is to understand and model the flux of terrestrial materials from land to sea – the origins, pathways, processes and ecosystem consequences – in the context of long‐term environmental change. The KWO consists of seven gauged watersheds and a network of observation sites spanning from land to sea and along drainage gradients within catchments. Time‐series datasets include year‐round measurements of weather, soil hydrology, streamflow, aquatic biogeochemistry, microbial ecology and nearshore oceanographic conditions. Sensor measurements are recorded every 5 min and water samples are collected approximately monthly. Additional observations are made during high‐flow conditions. We used remote sensing to map watershed terrain, drainage networks, soils and terrestrial ecosystems. The watersheds range in size from 3.2 to 12.8 km2, with varying catchment characteristics that influence hydrological and biogeochemical responses. Despite local variation, the overall study area is a global hotspot for yields of dissolved organic carbon, dissolved organic nitrogen and dissolved iron at the coastal margin. This observatory helps fill an important gap in the global network of observatories, in terms of spatial location (central coast of BC), climate (temperate oceanic), hydrology (very high runoff, pluvial regime), geology (igneous intrusive, glacially scoured), vegetation (bog rainforest) and soils (large stores of organic carbon).
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 | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".