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Record W2102472925 · doi:10.1139/f07-122

Perspectives on marine zooplankton lipids

2007· article· en· W2102472925 on OpenAlexaffvenue
Gerhard Kattner, Wilhelm Hagen, RF Lee, Robert G. Campbell, Don Deibel, Stig Falk‐Petersen, Martin Graeve, Benni Winding Hansen, Hans-Jürgen Hirche, Sigrún Huld Jónasdóttir, M L Madsen, Patrick Mayzaud, Dörthe C. Müller‐Navarra, Peter D. Nichols, GA Paffenhöfer, David W. Pond, Hiroaki Saito, Dorothea Stübing, Patti Virtue

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMemorial University of Newfoundland
FundersDeutsche Forschungsgemeinschaft
KeywordsTrophic levelFood webBiologyEcologyMembrane lipidsZooplanktonBiochemistryMembrane

Abstract

fetched live from OpenAlex

We developed new perspectives to identify important questions and to propose approaches for future research on marine food web lipids. They were related to (i) structure and function of lipids, (ii) lipid changes during critical life phases, (iii) trophic marker lipids, and (iv) potential impact of climate change. The first addresses the role of lipids in membranes, storage lipids, and buoyancy with the following key question: How are the properties of membranes and deposits affected by the various types of lipids? The second deals with the importance of various types of lipids during reproduction, development, and resting phases and addresses the role of the different storage lipids during growth and dormancy. The third relates to trophic marker lipids, which are an important tool to follow lipid and energy transfer through the food web. The central question is how can fatty acids be used to identify and quantify food web relationships? With the fourth, hypotheses are presented on effects of global warming, which may result in the reduction or change in abundance of large, lipid-rich copepods in polar oceans, thereby strongly affecting higher trophic levels. The key question is how will lipid dynamics respond to changes in ocean climate at high latitudes?

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 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 score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.195
Teacher spread0.182 · 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

Citations110
Published2007
Admission routes2
Has abstractyes

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