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Acidity versus habitat structure as regulators of littoral microcrustacean assemblages

2007· article· en· W2069471605 on OpenAlexaffabout
Bjørn Walseng, Norman D. Yan, Trevor W. Pawson, Olav Skarpaas

Bibliographic record

VenueFreshwater Biology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsYork UniversityMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsLittoral zoneMacrophyteHabitatEcologyDominance (genetics)BiologyCanonical correspondence analysisZooplanktonHydrobiologyFaunaCopepodSpecies richnessCrustaceanAquatic environment

Abstract

fetched live from OpenAlex

Summary 1. Emergence traps were set overnight on the sediment surface to sample the littoral microcrustaceans of 22 Canadian Shield lakes that ranged in pH from 4.56 to 6.92. Traps were randomly allocated in quintuplicate in both wave‐washed sandy habitats where pipewort ( Eriocaulon septangulare ) was the dominant macrophyte (termed pipewort habitats), and more protected habitats dominated by floating‐leaved macrophytes (termed floating‐leaved habitats). 2. In total, 50 cladoceran and 22 copepod species were found, with 16–45 species in each lake. Lakes that had never acidified exhibited a more diverse fauna than lakes that had acidified. 3. There were only minor differences between the numbers of species found in floating‐leaved versus pipewort habitats except for one lake. Non‐chydorid cladoceran, chydorids and copepods constituted 45%, 26% and 29% of the total number of individuals, respectively. 4. Based on presence/absence, dominance scores and frequency of occurrences of species, the microcrustacean faunal composition was similar in the two habitats. A detrended correspondence analysis confirmed that there was no separation between the two main types of habitat, and that pH was the parameter most strongly correlated with the dominant microcrustacean compositional gradient among lakes.

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 score0.363
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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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

Citations18
Published2007
Admission routes2
Has abstractyes

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