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Record W3032597291 · doi:10.1111/jpy.13037

Gas Composition of Developing Pneumatocysts in Bull Kelp<i>Nereocystis luetkeana</i>(Phaeophyceae)<sup>1</sup>

2020· article· en· W3032597291 on OpenAlexafffund
Lauran M. Liggan, Patrick T. Martone

Bibliographic record

VenueJournal of Phycology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyComposition (language)KelpGas compositionThallusAlgaeBuoyancyEcologyOceanographyBotanyGeologyPhysics

Abstract

fetched live from OpenAlex

The subtidal kelp Nereocystis luetkeana (hereafter Nereocystis ) maintains an upright stature by producing a single gas‐filled float (pneumatocyst) that provides buoyancy. The ability of Nereocystis pneumatocysts to inflate with gas underwater is peculiar, and the gas composition of pneumatocysts has been the topic of several studies over the last 100 years. Past studies of pneumatocyst gases only examined large sporophytes, leaving open questions about the origins of these gases and how gas composition may change during development. In this study, we use developmental time as a means to understand the origin and physiological mechanisms that give rise to different gases within Nereocystis pneumatocysts. Total gas composition was measured across a range of pneumatocyst sizes (5–725 mL). Contrary to previous studies that documented pneumatocyst gas concentrations to be similar to air, this study found internal gas levels of CO, CO 2 , and O 2 to be 1.2 ± 0.8%, 0.6 ± 0.2%, and 59.9 ± 13.6%, respectively. Our data suggest that the composition of gases does not change as pneumatocysts grow and that the rate of each gas added is approximately proportional to changes in pneumatocyst volume. Therefore, cells constituting the pneumatocyst wall are likely producing more gas (per surface area) to fill pneumatocysts as they expand, maintaining proportional gas composition and corresponding internal pressure.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.229
Teacher spread0.215 · 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 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

Citations4
Published2020
Admission routes2
Has abstractyes

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