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Record W2137987934 · doi:10.4319/lo.2009.54.5.1530

Towards understanding the relationship between wind conditions and plankton patchiness

2009· article· en· W2137987934 on OpenAlexafffundabout
E. A. Blukacz, Brian J. Shuter, W. G. Sprulesc

Bibliographic record

VenueLimnology and Oceanography · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of TorontoFisheries and Oceans Canada
FundersConnaught FundUniversity of TorontoMinistry of Natural ResourcesGreat Lakes Fishery Commission
KeywordsZooplanktonTransectBayEnvironmental scienceWind speedWind directionOceanographyWesterliesPrevailing windsSampling (signal processing)PlanktonStratification (seeds)Atmospheric sciencesGeologyBiology

Abstract

fetched live from OpenAlex

We used (1) water temperature (a proxy for water movement), (2) chlorophyll (a proxy for phytoplankton), and (3) zooplankton (bulk, small, and large size classes) to investigate the relationship between changing wind conditions and spatial patterns along linear transects (n 5 150) in two basins, South Arm and Annie Bay, of Lake Opeongo (Ontario, Canada). The basins have similar biological characteristics, but South Arm is larger and is oriented along the prevailing westerly wind direction. Large‐scale patterns (.1 km) were described with an accumulation index, and wavelet analysis was used to describe small‐scale patterns (≪1 km). Spatial descriptors were correlated with five descriptors of wind conditions: (1) wind force, (2) scalar wind speed, (3) vector wind speed, (4) wind persistence, and (5) wind direction. Persistent westerly winds in South Arm resulted in more downwind accumulation of warm water and total and large‐bodied zooplankton than in Annie Bay, while chlorophyll and small zooplankton did not show consistent downwind accumulation. The predominance of small‐scale variability, particularly in large zooplankton, increased in the South Arm as the persistence and strength of westerly winds blowing parallel to the sampling transects increased. Only temperature showed such a pattern in the smaller Annie Bay. These patterns were not related to winds blowing at the time of sampling but rather to those blowing up to 12 h before sampling. Our observations provide a basis for future consideration of how simple surface winds may actually shape the nature of trophic interactions in lake ecosystems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.368

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)0.0000.000

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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations52
Published2009
Admission routes3
Has abstractyes

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