MétaCan
Menu
Back to cohort
Record W2808251808 · doi:10.1073/pnas.1804351115

Primary productivity below the seafloor at deep-sea hot springs

2018· article· en· W2808251808 on OpenAlexfundno aff
Jesse McNichol, Hryhoriy Stryhanyuk, Sean P. Sylva, François Thomas, Niculina Musat, Jeffrey S. Seewald, Stefan M. Sievert

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersEuropean Regional Development FundDirectorate for GeosciencesDivision of Ocean SciencesHelmholtz-Zentrum für UmweltforschungCanadian Meteorological and Oceanographic SocietyNational Science FoundationGovernment of CanadaNatural Sciences and Engineering Research Council of CanadaWoods Hole Oceanographic InstitutionNational Aeronautics and Space Administration
KeywordsSeafloor spreadingGeologyPrimary productivityOceanographyProductivityDeep seaPrimary (astronomy)Environmental scienceEcosystemEcology

Abstract

fetched live from OpenAlex

Significance The existence of a chemosynthetic subseafloor biosphere was immediately recognized when deep-sea hot springs were discovered in 1977. However, quantifying how much new carbon is fixed in this environment has remained elusive. In this study, we incubated natural subseafloor communities under in situ pressure/temperature and measured their chemosynthetic growth efficiency and metabolic rates. Combining these data with fluid flux and in situ chemical measurements, we derived empirical constraints on chemosynthetic activity in the natural environment. Our study shows subseafloor microorganisms are highly productive (up to 1.4 Tg C produced yearly), fast-growing (turning over every 17–41 hours), and physiologically diverse. These estimates place deep-sea hot springs in a quantitative framework and allow us to assess their importance for global biogeochemical cycles.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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 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

Citations117
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207