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Record W6889751929 · doi:10.25976/pefk-dj94

Sturgeon River Watershed Aquatic Ecosystem Assessment

2025· dataset· en· W6889751929 on OpenAlexaboutno aff

Bibliographic record

VenueDataStream · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedAquatic ecosystemLake sturgeonWater qualityAcipenserEcosystemWatershed managementSampling (signal processing)Hydrology (agriculture)

Abstract

fetched live from OpenAlex

The North Saskatchewan Watershed Alliance (NSWA) is responsible for making watershed management recommendations to local watershed partners and the Government of Alberta. The Sturgeon River (SR) is one of the 12 sub-watersheds within the larger North Saskatchewan River (NSR) watershed. Although the SR watershed covers a relatively large portion of the NSR watershed, comprehensive information regarding the aquatic ecosystem is not available. Thus, the North Saskatchewan Watershed Alliance (NSWA) commissioned CPP Environmental to conduct this survey to create a baseline and status regarding the aquatic ecosystems along the SR. The scope of this project included multiple ecosystem components, including water quality, physical habitat, macroinvertebrate community, and fish community. The purpose of measuring all of these components is to obtain a comprehensive view of the SR aquatic ecosystems, which each are communities of living organisms and their physical and chemical environment. The Sturgeon River (SR) was surveyed at twelve sampling stations distributed throughout the length of the river, as well as the main tributaries. At each sampling station on the SR, physical habitat, water quality, vegetation, fish, and macroinvertebrate surveys took place. In the tributaries, only water quality was measured. Water quality variables analyzed included nutrients (phosphorus and nitrogen), dissolved oxygen, suspended solids, pesticides, metals, and salts.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.664
Threshold uncertainty score0.668

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.005

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.

Opus teacher head0.014
GPT teacher head0.296
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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