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Record W4400386531 · doi:10.1101/2024.07.03.601798

Reproductive success of inbred strain MV31 of the ctenophore <i>Mnemiopsis leidyi</i> in a self-sustaining inland laboratory culture system

2024· preprint· en· W4400386531 on OpenAlexafffund
Pranav Garg, Cameron Frey, William E. Browne, Steven S. Plotkin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsUniversity of British Columbia
FundersMitacs
KeywordsStrain (injury)BiologyInbred strainZoologyGeneticsAnatomy

Abstract

fetched live from OpenAlex

Abstract Ctenophores are an attractive lineage for studying animal evolution due to their early divergence from other metazoans. Among Ctenophora, Mnemiopsis leidyi is a model system for developmental, cellular, molecular genetic, and evolutionary studies. Until recently, many of these studies were conducted on wild-caught animals, limiting access to researchers on the coast. Here we present significant advancements towards culturing M. leidyi in laboratories without coastal access, enabling its wider use as an experimental and genetic model system. We detail updated feeding regimes that take advantage of co-culturing Brachionus rotifers with Apocyclops copepods, and quantify the reproductive output of our M. leidyi lab strain on this diet. Our updated feeding regime maintains reproductive fitness comparable to wild-caught individuals. Importantly, we have eliminated the logistical complexities and costs of regularly feeding live larval fish to M. leidyi . Our updated protocols make it feasible to maintain continuous ctenophore cultures independent of access to both coastal populations of wild M. leidyi and larval fish culturing facilities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes2
Has abstractyes

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