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Record W3217590584 · doi:10.3289/geomar_rep_ns_59_20

RV SONNE Fahrtbericht/Cruise Report SO268 - Assessing the Impacts of Nodule Mining on the Deep-sea Environment: NoduleMonitoring, Manzanillo (Mexico) – Vancouver (Canada), 17.02. – 27.05.2019

2021· report· en· W3217590584 on OpenAlexaboutno aff
Matthias Haeckel, Петер Линке

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCruiseOceanographyEnvironmental scienceSedimentPlumeGeologyPhysical geographyMeteorologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Cruise SO268 is fully integrated into the second phase of the European collaborative JPI-Oceans project MiningImpact and is designed to assess the environmental impacts of deep-sea mining of polymetallic nodules in the Clarion-Clipperton Fracture Zone (CCZ). In particular, the cruise aimed at conducting an independent scientific monitoring of the first industrial test of a pre-protoype nodule collector by the Belgian company DEME-GSR. The work includes collecting the required baseline data in the designated trial and reference sites in the Belgian and German contract areas, a quantification of the spatial and temporal spread of the produced sediment plume during the trials as well as a first assessment of the generated environmental impacts. However, during SO268 Leg 1 DEME-GSR informed us that the collector trials would not take place as scheduled due to unresolvable technical problems. Thus, we adjusted our work plan accordingly by implementing our backup plan. This involved conducting a small-scale sediment plume experiment with a small chain dredge to quantify the spatial and temporal dispersal of the suspended sediment particles, their concentration in the plume as well as the spatial footprint and thickness of the deposited sediment blanket on the seabed.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0030.001
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 head0.035
GPT teacher head0.320
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations26
Published2021
Admission routes1
Has abstractyes

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Same topicArctic and Russian Policy StudiesFrench-language works237,207