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Record W4323355561 · doi:10.21203/rs.3.rs-2648840/v1

Subfossil chironomid assemblages as indicators of remedial efficacy in the historically contaminated St. Lawrence River at Cornwall, Ontario

2023· preprint· en· W4323355561 on OpenAlexaffabout
Isaac Armstrong, Katherine E. Moir, Jeffrey J. Ridal, Brian F. Cumming

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsSt. Lawrence River Institute of Environmental SciencesQueen's University
Fundersnot available
KeywordsSubfossilRemedial educationEnvironmental scienceArchaeologyHydrology (agriculture)GeographyGeologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Long-term data are required to quantify the impacts of historic industrial pollution and subsequent remedial action on the nearshore benthic community in the St. Lawrence River Area of Concern at Cornwall, Ontario. Specifically, high-quality temporal records are needed to understand changes in benthic invertebrate assemblages in response to multiple possible drivers including industrial pollution, environmental heterogeneity, and climate warming. We compare long-term records of subfossil chironomid assemblages and geochemical variables among sediment cores from two Cornwall sites with differing pollution histories and a minimally disturbed downstream reference site. Chironomids were functionally absent from the Cornwall sediment cores when mercury and zinc concentrations were elevated. As metal concentrations decreased in more recent sediment intervals, chironomid abundance and the relative abundance of pollution-sensitive taxa increased. Recently deposited sediment in all three sediment cores display increased relative abundance of warm-water, macrophyte-associated taxa. We conclude that these temporal changes in chironomid assemblages provide evidence for ecological recovery for both of the impacted sites, consistent with the objectives of the current management strategy. These findings advance our understanding of industrial impacts on fluvial chironomid ecology, directly inform local management strategies, and further develop the application of chironomids as bioindicators for contaminated sediments.

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 score0.047
Threshold uncertainty score0.094

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 studies0.0020.001
Scholarly communication0.0010.000
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 head0.066
GPT teacher head0.351
Teacher spread0.285 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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