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Record W2767583145 · doi:10.1190/int-2017-1017-fe.1

Roles and responsibilities of the special-section editor

2017· article· en· W2767583145 on OpenAlexaff
Kurt J. Marfurt, Gerald Schuster, Sheral Danker, Balázs Németh, Huyen Bui, Hongliu Zeng

Bibliographic record

VenueInterpretation · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsBHP (Canada)
Fundersnot available
KeywordsSection (typography)Special sectionPhilosophyHistoryComputer scienceEngineeringEngineering physics

Abstract

fetched live from OpenAlex

Interpretation differs from most journals in that each issue is built largely on special sections that focus on integrated multidisciplinary approaches, techniques in exploration and production, on a specific technology, on a specific basin, or on a specific type of geologic process or feature common to multiple basins. Such special sections provide the reader with the perspective and expertise of authors from different institutions and disciplines focused on the same problem. Depending on the topic, the resulting collection of papers may exhibit diversity, consensus, or both.
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\nWhile the editorial board of a given journal may be able to identify a subset of candidate topics for special sections, in-depth knowledge of critical problems and of the key players working on such topics is usually beyond their areas of expertise. To address this need, Interpretation draws upon such experts to construct most special sections.
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\nIn addition to providing a focused collection of articles on a given topic, there are two additional advantages of the special-section format. First, the journal broadens the scope of contributors beyond those whose career advancement (typically in academia and in government laboratories) requires them to publish in peer-reviewed journals. Typically, the special-section organizers will identify oral presentations and expanded abstracts presented at meetings (such as SEG, AAPG, URTeC, OTC, and SPE) and ask the authors to expand upon their work and generate a more comprehensive document. Apart from a few technology companies, most companies do not use external publications as a component of career development. For this reason, requests by a respected special editorial team provides not only recognition to the authors, but also the justification of the authors’ management to allow them to build on their work. Nearly all companies include the act of supporting communities among their core values. Contributing to a special section is a great way for professionals to demonstrate that value in action. Other advantages include the perspectives provided by such special-section authors, editors, and reviewers that draw in a diverse suite of geoscientists and engineers that truly represent the interpretation community.
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\nWhere there are advantages, there are also disadvantages. First, the vast majority of special-section editors are “new to the job,” never having served on an editorial board of a peer-reviewed scientific journal. The primary objective of this “From the Editor” note is to clarify the roles and responsibilities of this task. Second, many of the selected reviewers may never have had the opportunity to review a paper for publication. They too are new to the job. The August issue of the From the Editor column (Bui et al., 2017) titled “Qualities of a good reviewer” directly addresses this challenge. In this article, we encourage the reviewers to help the authors to construct a quality, well-referenced paper because many of them, too, are new to the job. The third disadvantage is that the special-section format skews the citation-index metrics. Obviously, if the special-section editors identify a good oral presentation and request the author to develop a full-length paper, the chances of acceptance are higher, thus lowering the citation index score. More on this conundrum will be found in the From the Editor column in the February 2018 issue of Interpretation.
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\nThe remainder of this short article addresses the issues specific to the special-section editors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.207

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)0.0000.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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