MétaCan
Menu
Back to cohort
Record W1939479654 · doi:10.1139/cjfas-2012-0431

Changes in water chemistry associated with beaver-impounded coastal marshes of eastern Georgian Bay

2013· article· en· W1939479654 on OpenAlexafffundvenueabout
Amanda Fracz, Patricia Chow‐Fraser

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsMcMaster University
FundersParks CanadaMinistry of Natural Resources
KeywordsBayMarshWetlandGeorgianBiotaEnvironmental scienceBeaverOceanographyHydrology (agriculture)EcologyGeologyBiology

Abstract

fetched live from OpenAlex

Coastal marshes of eastern Georgian Bay contain unique water chemistry that reflects mixing between the relatively ion-rich waters of Georgian Bay and the relatively ion-poor water draining the Canadian Shield landscape. These unique chemical characteristics may be dramatically altered when wetlands become hydrologically disconnected from Georgian Bay through beaver activity. We sampled 35 coastal marshes in Georgian Bay, 17 of which had beaver impoundments built at the outlet of the coastal wetland. Impounded marshes had significantly higher total phosphorus (30.2 versus 15.3 μg·L −1 , p = 0.0015), soluble reactive phosphorus, (13.33 versus 3.7 μg·L −1 , p ≤ 0.0001), total suspended solids (15.5 versus 2.1 mg·L −1 , p ≤ 0.0001), turbidity (5.4 versus 1.6, p = 0.0004), and chlorophyll (6.2 versus 1.9 μg·L −1 , p = 0.0004), but significantly lower pH (5.57 versus 6.95, p ≤ 0.0001), nitrates (0.03 versus 0.04 mg·L −1 , p = 0.0416), and conductivity (47 versus 134 μS·cm −1 , p ≤ 0.0001), indicative of reduced mixing with Georgian Bay. The mosaic of chemical conditions and altered hydrological connectivity associated with beaver impoundments in coastal marshes of Georgian Bay may affect the distribution of other wetland biota, and further studies should be conducted to ascertain these impacts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.166
Teacher spread0.155 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2013
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicEcology and biodiversity studiesFrench-language works237,207