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Record W3128261565 · doi:10.1093/plankt/fbab074

The effect of phytoplankton properties on the ingestion of marine snow by <i>Calanus pacificus</i>

2021· article· en· W3128261565 on OpenAlexaff
Grace F. Cawley, Moira Décima, Andrea Mast, Jennifer C. Prairie

Bibliographic record

VenueJournal of Plankton Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersNational Science Foundation of Sri Lanka
KeywordsPhytoplanktonCopepodThalassiosira weissflogiiMarine snowPlanktonZooplanktonCalanusOceanographyDiatomBiologyTrophic levelAcartia tonsaSnowEcologyEnvironmental scienceCrustaceanNutrientWater columnGeology

Abstract

fetched live from OpenAlex

Abstract Marine snow, formed through the aggregation of phytoplankton and other organic matter, can be consumed by various types of zooplankton, affecting both planktonic trophic dynamics and the export of carbon to depth. This study focuses on how two factors—phytoplankton growth phase and species—affect copepod feeding on marine snow. To do this, we conducted a series of grazing experiments using gut pigment and stable isotope methods to quantify the ingestion of the copepod, Calanus pacificus, on both marine snow aggregates and individual phytoplankton. Results demonstrate that marine snow can represent a substantial food source for copepods, comparable to rates on individual phytoplankton. Moreover, we found that both the overall ingestion and the relative ingestion of aggregates vs. individual phytoplankton depended on phytoplankton growth phase for experiments conducted with the diatom Thalassiosira weissflogii. Although copepods consumed aggregates composed of Skeletonema marinoi at similar rates as those composed of T. weissflogii, no effect of growth phase was observed for S. marinoi. These findings suggest that marine snow can be an important source of nutrition for copepods, but that its role in planktonic food webs may differ depending on the phytoplankton community composition and the stage of phytoplankton blooms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.026
GPT teacher head0.255
Teacher spread0.228 · 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 designBench or experimental
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

Citations12
Published2021
Admission routes1
Has abstractyes

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