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Record W2275695859 · doi:10.1139/cjfas-2015-0231

Spatial patterns, trends, and the potential long-term impacts of tree harvesting on lake calcium levels in the Muskoka River Watershed, Ontario, Canada

2016· article· en· W2275695859 on OpenAlexaffvenueabout
Carolyn Reid, Shaun A. Watmough

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsTrent University
Fundersnot available
KeywordsWatershedEnvironmental scienceSurface runoffEcosystemAquatic ecosystemAgricultureHydrology (agriculture)EcologyBiologyGeology

Abstract

fetched live from OpenAlex

The issue of calcium (Ca) decline in surface waters of eastern Canada is an emerging concern that may be made worse by timber harvesting. In the Muskoka River Watershed (MRW) in Ontario, the mean lake Ca concentration in 104 lakes decreased by 30% since the 1980s, with the rate of decrease slowing over time consistent with changes in lake sulfate (SO 4 ) as the region recovers from acid deposition. Recent data suggested that smaller lakes, at higher elevation, in smaller catchments with higher runoff that are minimally impacted by the influence of roads and agriculture are associated with lower Ca concentrations and thus are the lakes most at risk of amplified Ca depletion. Using proposed annual allowable harvest cuts from 10-year forest management plans, 38% of 364 lakes assessed in the MRW will fall below a reported critical 1 mg·L –1 Ca threshold compared with just 8% in the absence of future harvesting. It is concluded that Ca decline poses a serious threat to aquatic ecosystems and should be taken into consideration in future forest management plans.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.198
Teacher spread0.182 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations23
Published2016
Admission routes3
Has abstractyes

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