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Record W2212218366 · doi:10.20396/td.v9i2.8637392

A brief history of scientific ocean drilling programs / Breve história dos programas científicos

2015· article· pt· W2212218366 on OpenAlexaff
Michael J. Passow, Hélder Pereira, Leslie Peart

Bibliographic record

VenueTerrae Didatica · 2015
Typearticle
Languagept
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsDeep River Science Academy
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Recentemente, o Brasil aderiu ao Integrated Ocean Drilling Program, que realiza expedições científicas em todo o mundo. Isso permitiu que cientistas brasileiros pudessem participar na exploração do fundo do oceano nas imediações de uma fossa oceânica ao largo da Costa Rica. A perfuração do fundo do oceano para fins científicos foi proposta pela primeira vez em 1957 e começou na década de 60 do século XX. Em 1968, naquela que foi apenas a sua terceira expedição, o “Glomar Challenger” recuperou amostras de rochas e sedimentos dos dois lados da dorsal média-oceânica no Atlântico Sul, e revelou a expansão dos fundos oceânicos. Antes da capacidade de perfurar o fundo do oceano a grandes profundidades, os cientistas apenas tinham à sua disposição testemunhos obtidos com amostradores de tipo “pistão”. Os microfósseis obtidos a partir desses testemunhos têm permitido fazer muitas descobertas sobre as oscilações climáticas da Terra. Nas expedições realizadas atualmente pelo “JOIDES Resolution”, pelo “Chikyu”, e pelas plataformas de perfuração das missões específicas, continuam a surgir descobertas impressionantes que têm contribuído para melhorar a compreensão da história geológica do nosso planeta. Nos últimos anos, vários educadores têm acompanhado os cientistas durante as expedições, e criaram vários materiais educativos e de divulgação destinadas a estudantes e ao público em geral.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.015
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.041
GPT teacher head0.232
Teacher spread0.191 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2015
Admission routes1
Has abstractyes

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