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Record W2151485158 · doi:10.1071/mf08260

Macroinvertebrates on coarse woody debris in the littoral zone of a boreal lake

2009· article· en· W2151485158 on OpenAlexaffabout
Patricia Glaz, Christian Nozais, Dominique Arseneault

Bibliographic record

VenueMarine and Freshwater Research · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsBorealCoarse woody debrisEcologyTaigaRiparian zoneLittoral zoneCobbleCommunity structureEnvironmental scienceInvertebratePhysical geographyGeographyHabitatBiology

Abstract

fetched live from OpenAlex

Logging activity was a regular practice in the boreal forest of Quebec during the 19th century and may have had an impact on the temporal dynamics of aquatic coarse woody debris (CWD) and associated organisms. The dynamics of white cedar (Thuja occidentalis) CWD inputs from the riparian environment in a boreal lake in Eastern Quebec, Canada, over the past 350 years were reconstructed and differences in the macroinvertebate communities according to CWD age, season of sampling (spring, summer and autumn), depth and site were investigated. It was hypothesised that CWD macroinvertebrate community structure would change with CWD age, season and depth, but not among sites. No significant correlation was found between CWD age and macroinvertebrate densities and taxa number. The macroinvertebrate community was highly variable in space and time. Season was the main factor influencing taxa composition and the relative densities of individuals. The mean density was more than twofold greater in autumn than in spring and summer (1046, 1049 and 2335 individuals m–2 in spring, summer and autumn respectively). Density and taxa number decreased with depth, but site did not appear to influence the community. As CWD inputs increased during the log-driving period, impacts on macroinvertebrate communities were likely to be important and should be documented across the boreal zone.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.904

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.276
Teacher spread0.226 · 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.

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

Citations14
Published2009
Admission routes2
Has abstractyes

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