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Record W2981903415 · doi:10.5194/hess-2019-438

Ionic aluminium concentrations exceed thresholds for aquatic health in Nova Scotian rivers

2019· article· en· W2981903415 on OpenAlexafffundabout
Shannon Sterling, Sarah MacLeod, Lobke Rotteveel, Kristin Hart, Thomas A. Clair, Edmund A. Halfyard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsDalhousie University
FundersFisheries and Oceans CanadaFondation Pour La Conservation Du Saumon AtlantiqueAtlantic Canada Opportunities Agency
KeywordsNova scotiaSalmoAquatic ecosystemEnvironmental scienceDissolved organic carbonEnvironmental chemistryEcologyChemistryFisheryOceanographyBiologyGeologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract. Cationic aluminium species are toxic to terrestrial and aquatic life. Despite decades of acid emission reductions, accumulating evidence shows that freshwater acidification recovery is delayed in locations such as Nova Scotia, Canada. Further, spatial and temporal patterns of labile cationic forms of aluminium (Ali) remain poorly understood. Here we increase our understanding of Ali spatial and temporal patterns by measuring Ali concentrations in ten streams in acid-sensitive areas of Nova Scotia over a four-year time period. We observe widespread and frequent occurrences of Ali concentrations that exceed toxic thresholds (> 15 μg L−1). Ali patterns appear to be driven by known Ali drivers – pH, dissolved organic carbon, dissolved aluminium, and calcium – but the dominant driver and temporal patterns vary by catchment. Our results demonstrate that elevated Ali remains a threat to aquatic ecosystems. For example, our observed Ali concentrations are potentially harmful to the biologically, economically, and culturally significant Atlantic salmon (Salmo salar).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.243
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.274
Teacher spread0.259 · 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 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

Citations4
Published2019
Admission routes3
Has abstractyes

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