Water quality, habitat, land use, waterfowl, and fish community data from wetlands in the Boreal Transition Zone, Alberta
Bibliographic record
Abstract
Water quality, environment, agricultural land use, waterfowl, and fish community data were collected from wetlands in four landscapes (West Peace Lowlands, East Peace Lowlands, West Central, and East Central) in the Boreal Transition Zone (BTZ) of Alberta (AB) and with one landscape in British Columbia (East Peace Lowlands, BC) during May and August over three years (2005–2007). This data set is unique in that it provides comprehensive water quality sampling, habitat conditions, and associated breeding and molting waterfowl data over a three-year period, which includes a drought year. Land use in the BTZ has rapidly changed with at least 73% of the BTZ, particularly in Saskatchewan, AB, being converted from forest to agriculture since the early 20th century. The BTZ has experienced a drying trend over the past 60 years with the lowest cumulative effective precipitation since 1943 with a −2323-mm precipitation deficit recorded in 2006. A total of 723 and 489 wetlands were surveyed for waterfowl abundance during the breeding and molting seasons, respectively. A subset of 214 and 213 wetlands in May and August, respectively, was further sampled for habitat, limnology, and agricultural encroachment surrounding the wetland. A subset of 25 wetlands was sampled in 2006 for fish abundance data. This data set has been used in several papers in assessing the impact of drought and agricultural impact on water quality and wetland conditions, as well as drought and land use impacts on habitat associations between breeding and molting waterfowl and wetland condition.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".