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Record W4415253844 · doi:10.1007/s10021-025-01015-1

Quantifying Benthic Flux of Mysis Biomass Through Diel Vertical Migration at the Ecosystem Scale

2025· article· en· W4415253844 on OpenAlexaboutno aff
Brian P. O’Malley, Georgia L. Hoffman, Rosaura J. Chapina, Jason D. Stockwell, Collin J. Farrell

Bibliographic record

VenueEcosystems · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersGreat Lakes Fishery Commission
KeywordsPelagic zoneBenthic zoneDiel vertical migrationBiomass (ecology)HabitatEcosystem

Abstract

fetched live from OpenAlex

Abstract Mysis diluviana is a macroinvertebrate that couples benthic and pelagic habitats on a daily timescale through diel vertical migration (DVM). However, quantifying how much Mysis biomass is exchanged between benthic and pelagic habitats at an ecosystem scale is difficult because of sampling limitations and variability in Mysis DVM behavior related to light and depth. Although Mysis are benthic-pelagic migrators, a portion remains pelagic during the day offshore in Lake Ontario, partially contradicting the assumption of population-level DVM over deep areas. To estimate the amount of biomass transferred from benthic to pelagic habitat via DVM in Lake Ontario, we estimated the portion of pelagic biomass at night originating from benthic habitat as the difference between night and day pelagic estimates from net tows along a bathymetric depth gradient. We then modeled the portion as a function of depth, extrapolated these depth-dependent estimates to an existing lake wide night-pelagic dataset, and summed amounts across depth strata. We estimated more biomass was transferred from benthic to pelagic habitat at intermediate lake depths (100–160 m) despite greater offshore (> 180 m) night-pelagic biomass. Our results suggest ways to improve estimates of Mysis habitat coupling and how to account for important factors such as depth and light for modeling Mysis DVM behavior at the population- and ecosystem-levels.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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