MétaCan
Menu
Back to cohort
Record W2009099950 · doi:10.1139/f01-142

Calcium declines in northeastern Ontario lakes

2001· article· en· W2009099950 on OpenAlexvenueaboutno aff
Wendel Keller, Sweta Dixit, Jocelyne Heneberry

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsAcid depositionEnvironmental scienceDiatomLeaching (pedology)Deposition (geology)Environmental chemistryCalciumWater qualityPaleolimnologyAcid rainSedimentEcologyHydrology (agriculture)ChemistryGeologySoil waterBiologySoil science

Abstract

fetched live from OpenAlex

Thousands of lakes in northeastern Ontario, Canada, have been acidified by sulphur deposition associated with emissions from the Sudbury area metal smelters. However, water quality improvements including increased pH and reduced sulphate concentrations have followed large reductions in Sudbury emissions that were implemented, beginning in the 1970s. Substantial decreases in Ca concentrations accompanied these other changes in lakewater chemistry. Monitoring of 38 lakes 20–128 km from Sudbury showed declines in Ca concentrations, averaging 2.7 µeq·L –1 ·year –1 , over the period 1981–1999. Declines were particularly apparent during the 1990s, averaging 3.8 µeq·L –1 ·year –1 . Paleolimnological reconstructions of the long-term Ca patterns in six lakes suggest that general lakewater Ca declines occurred through much of the 20th century. Comparison of recent measured Ca concentrations in 16 lakes with diatom-inferred pre-industrial Ca concentrations indicates that overall decreases in Ca have been large, averaging 74.6 µeq·L –1 or 46%. Long-term Ca patterns may reflect a combination of factors including climatic changes, forest harvesting activities, and leaching by acid deposition, the effects of which we can not separate. Calcium declines have biological implications that will need to be considered in the development of appropriate targets as these lakes continue to recover from acidification.

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 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 score0.701
Threshold uncertainty score0.999

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)0.0020.000

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 head0.032
GPT teacher head0.232
Teacher spread0.200 · 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

Citations87
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicHeavy metals in environmentFrench-language works237,207