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Record W4248130821 · doi:10.5194/gmd-2017-24

The Palaeoclimate and Terrestrial Exoplanet Radiative Transfer Model Intercomparison Project (PALAEOTRIP): experimental design and protocols

2017· preprint· en· W4248130821 on OpenAlexafffund
Colin Goldblatt, Lucas Kavenagh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsOcean Networks Canada SocietyUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsExoplanetBespokeRadiative transferEnvironmental scienceClimate modelComputer scienceMeteorologyClimate changeGeographyGeologyPhysicsPlanetAstronomyPolitical science

Abstract

fetched live from OpenAlex

Abstract. Accurate radiative transfer calculation is fundamental to all climate modelling. For deep palaeoclimate, and increasingly terrestrial exoplanet climate science, this brings both the joy and the challenge of exotic atmospheric compositions. The challenge here is that most standard radiation codes for climate modelling have been developed for modern atmospheric conditions, and may perform poorly away from these. The palaeoclimate or exoclimate modeller must either rely on these or use bespoke radiation codes, and in both cases rely on either blind faith or ad hoc testing of the code. In this paper, we describe the protocols for the Palaeoclimate and Terrestrial Exoplanet Radiative Transfer Model Intercomparison Project (PALAEOTRIP) to systematically address this. This will compare as many radiation codes used for palaeoclimate or exoplanets as possible, with the aim to constrain the ranges of far-from-modern atmospheric compositions in which the codes perform well. This paper describes the experimental protocol and invites community participation in the project through 2017.

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)0.017
metaresearch head score (Gemma)0.020
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: Not applicable
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0360.012

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 head0.088
GPT teacher head0.320
Teacher spread0.232 · 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
GenreProtocol

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
Published2017
Admission routes2
Has abstractyes

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