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Record W4280624653 · doi:10.3389/fmars.2022.866422

Integrating Multidisciplinary Observations in Vent Environments (IMOVE): Decadal Progress in Deep-Sea Observatories at Hydrothermal Vents

2022· article· en· W4280624653 on OpenAlexaffabout
Marjolaine Matabos, Thibaut Barreyre, S. Kim Juniper, Mathilde Cannat, Deborah S. Kelley, Joan M. Alfaro‐Lucas, Valérie Chavagnac, Ana Colaço, J. Escartı́n, Elva Escobar‐Briones, Daniel J. Fornari, Jörg Hasenclever, Julie A. Huber, Agathe Laës, Nadine Lantéri, Lisa A. Levin, Steve Mihály, E. L. Mittelstaedt, Florence Pradillon, Pierre‐Marie Sarradin, Jozée Sarrazin, Beatrice Tomasi, Clément Vic

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsOcean Networks Canada SocietyUniversity of Victoria
FundersFundação para a Ciência e a TecnologiaUniversitetet i BergenCentre National de la Recherche ScientifiqueEuropean CommissionAgence Nationale de la RechercheISblueUniversidade dos AçoresNational Science Foundation
KeywordsContext (archaeology)OceanographyDeep seaHydrothermal ventClimate changeEnvironmental scienceLead (geology)Ocean acidificationEnvironmental changeEarth scienceGeologyEcosystemEcologyPaleontologyHydrothermal circulation

Abstract

fetched live from OpenAlex

The unique ecosystems and biodiversity associated with mid-ocean ridge (MOR) hydrothermal vent systems contrast sharply with surrounding deep-sea habitats, however both may be increasingly threatened by anthropogenic activity (e.g., mining activities at massive sulphide deposits). Climate change can alter the deep-sea through increased bottom temperatures, loss of oxygen, and modifications to deep water circulation. Despite the potential of these profound impacts, the mechanisms enabling these systems and their ecosystems to persist, function and respond to oceanic, crustal, and anthropogenic forces remain poorly understood. This is due primarily to technological challenges and difficulties in accessing, observing and monitoring the deep-sea. In this context, the development of deep-sea observatories in the 2000s focused on understanding the coupling between sub-surface flow and oceanic and crustal conditions, and how they influence biological processes. Deep-sea observatories provide long-term, multidisciplinary time-series data comprising repeated observations and sampling at temporal resolutions from seconds to decades, through a combination of cabled, wireless, remotely controlled, and autonomous measurement systems. The three existing vent observatories are located on the Juan de Fuca and Mid-Atlantic Ridges (Ocean Observing Initiative, Ocean Networks Canada and the European Multidisciplinary Seafloor and water column Observatory). These observatories promote stewardship by defining effective environmental monitoring including characterizing biological and environmental baseline states, discriminating changes from natural variations versus those from anthropogenic activities, and assessing degradation, resilience and recovery after disturbance. This highlights the potential of observatories as valuable tools for environmental impact assessment (EIA) in the context of climate change and other anthropogenic activities, primarily ocean mining. This paper provides a synthesis on scientific advancements enabled by the three observatories this last decade, and recommendations to support future studies through international collaboration and coordination. The proposed recommendations include: i) establishing common global scientific questions and identification of Essential Ocean Variables (EOVs) specific to MORs, ii) guidance towards the effective use of observatories to support and inform policies that can impact society, iii) strategies for observatory infrastructure development that will help standardize sensors, data formats and capabilities, and iv) future technology needs and common sampling approaches to answer today’s most urgent and timely questions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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 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

Citations21
Published2022
Admission routes2
Has abstractyes

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