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Littoral microcrustacea in Lake 302S in the Experimental Lakes Area of Canada: acidification and recovery

2000· article· en· W2043197567 on OpenAlexaffabout
Brenda J. Hann, Michael A. Turner

Bibliographic record

VenueFreshwater Biology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsFisheries and Oceans CanadaUniversity of Manitoba
Fundersnot available
KeywordsCladoceraSpecies richnessLittoral zoneCopepodAbundance (ecology)EcologyBiologyZooplanktonBranchiopodaHydrobiologyBosminaCrustaceanAquatic environment

Abstract

fetched live from OpenAlex

Summary The littoral microcrustacean community (Cladocera and Copepoda) was examined from 1989 to 1991 in a lake experimentally acidified to pH 4.5, and from 1992 to 1997 during the early stages of pH recovery. Cladoceran abundance declined significantly from 1989 to 1991 (pH 4.5), but species richness did not change. Acantholeberis curvirostris, Simocephalus serrulatus, Latona spp. (Latona setifera, L. parviremis) , and all species of chydorid Cladocera declined markedly in abundance while at pH 4.5. The abundance of cyclopoid copepods was low and Ceriodaphnia and calanoid copepods were absent. Recovery of the community was subsequently monitored as pH was incrementally changed to a target pH 5.1 in 1992 and 1993, and to 5.8 in 1994–97. Species richness remained unchanged. Chydorid cladocerans remained at low abundance in 1992, and only Chydorus cf. brevilabris increased substantially from 1993 to 1996. Non‐chydorid Cladocera increased in abundance in 1992, declined again in 1993, then gradually increased (mainly due to Ophryoxus gracilis ) in 1994–96. All species declined in 1997 as minnows recolonized the lake. The calanoid copepod Leptodiaptomus minutus was present in low numbers in 1997. The microcrustacean community in the littoral zone of Lake 302S has not yet shown consistent signs of recovery 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)0.000
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 score0.888
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 studies0.0000.000
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 head0.007
GPT teacher head0.198
Teacher spread0.190 · 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

Citations28
Published2000
Admission routes2
Has abstractyes

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