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Record W2201748480 · doi:10.1089/ast.2015.1113

A Strategy for Origins of Life Research

2015· article· en· W2201748480 on OpenAlexaff
Caleb Scharf, Nathaniel Virgo, Henderson James Cleaves, Masashi Aono, Nathanaël Aubert-Kato, Arsev Umur Aydınoğlu, Ana Barahona, Laura M. Barge, Steven A. Benner, Martin Biehl, Ramon Brasser, Christopher J. Butch, Kuhan Chandru, Leroy Cronin, Sebastian O. Danielache, Jakob Fischer, J. W. Hernlund, Piet Hut, Takashi Ikegami, Jun Kimura, Kensei Kobayashi, Carlos Mariscal, Shawn E. McGlynn, Brice Ménard, Norman H. Packard, Robert Pascal, Juli Peretό, Sudha Rajamani, Lana Sinapayen, Eric A. Smith, Christopher Switzer, Ken Takai, Feng Tian, Yuichiro Ueno, Mary A. Voytek, Olaf Witkowski, Hikaru Yabuta

Bibliographic record

VenueAstrobiology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsDalhousie University
FundersJet Propulsion LaboratoryCalifornia Institute of TechnologyJohn Templeton Foundation
KeywordsDomain (mathematical analysis)Framing (construction)Key (lock)Computer scienceSociologyHistoryArchaeologyMathematics

Abstract

fetched live from OpenAlex

Contents 1. Introduction 1.1. A workshop and this document 1.2. Framing origins of life science 1.2.1. What do we mean by the origins of life (OoL)? 1.2.2. Defining life 1.2.3. How should we characterize approaches to OoL science? 1.2.4. One path to life or many? 2. A Strategy for Origins of Life Research 2.1. Outcomes-key questions and investigations 2.1.1. Domain 1: Theory 2.1.2. Domain 2: Practice 2.1.3. Domain 3: Process 2.1.4. Domain 4: Future studies 2.2. EON Roadmap 2.3. Relationship to NASA Astrobiology Roadmap and Strategy documents and the European AstRoMap Appendix I Appendix II Supplementary Materials References.

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)0.028
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0040.014
Scholarly communication0.0100.021
Open science0.0030.010
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0200.008

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 head0.244
GPT teacher head0.418
Teacher spread0.173 · 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 designTheoretical or conceptual
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

Citations66
Published2015
Admission routes1
Has abstractyes

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