MétaCan
Menu
Back to cohort
Record W2130563451 · doi:10.1093/plankt/fbl074

Spatial heterogeneity and genetic variation in the copepod Neocalanus cristatus along two transects in the North Pacific sampled by the Continuous Plankton Recorder

2006· article· en· W2130563451 on OpenAlexaboutno aff
Richard R. Kirby, J. A. Lindley, Sonia Batten

Bibliographic record

VenueJournal of Plankton Research · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsTransectCopepodDiel vertical migrationBiologyEcologyOceanographyPlanktonSubarctic climatePopulationSpatial variabilityBayCrustaceanGeology

Abstract

fetched live from OpenAlex

We present a macrogeographic study of spatial heterogeneity in an important subarctic Pacific copepod and describe the first genetic analysis of population structure using Continuous Plankton Recorder (CPR) samples. Samples of Neocalanus cristatus were collected at a constant depth of ∼7 m from two CPR tow-routes, (i) an east–west ∼6500-km transect from Vancouver Island, Canada to Hokkaido Island, Japan, and (ii) a north–south transect of ∼2250 km from Anchorage, Alaska to Tacoma, Washington. Analysis of these samples revealed three features of the biology of N. cristatus. First, N. cristatus undergoes small-scale diel vertical migration that is larger among stages CV–adult (3–6 times more abundant at 7 m at night), than stages CI–CIV (only 2–4 times higher at night). Secondly, while there were no regions where N. cristatus did not appear, each transect sampled a few large-scale macrogeographic patches. Thirdly, an analysis of molecular variation, using a partial sequence of the N. cristatus cytochrome oxidase I gene, revealed that 7.3% (P < 0.0001) of the total genetic variation among N. cristatus sampled from macrogeographic patches by the CPR could be explained by spatial heterogeneity. We suggest that spatial heterogeneity at macrogeographic scales may be important in plankton evolution.

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)0.004
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 score0.384
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Plankton ResearchSame topicMarine and coastal ecosystemsFrench-language works237,207