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Record W1996475348 · doi:10.1126/science.1230260

Response to Comment on "Radiative Absorption Enhancements Due to the Mixing State of Atmospheric Black Carbon"

2013· letter· en· W1996475348 on OpenAlexaff
Christopher D. Cappa, T. B. Onasch, P. Massoli, Douglas R. Worsnop, T. S. Bates, Eben S. Cross, P. Davidovits, Jani Hakala, Katherine Hayden, B. T. Jobson, Katheryn R. Kolesar, D. A. Lack, B. M. Lerner, Shao‐Meng Li, Daniel Mellon, I. Nuaaman, Jason S. Olfert, Tuukka Petäjä, Patricia K. Quinn, Chen Song, R. Subramanian, E. J. Williams, R. A. Zaveri

Bibliographic record

VenueScience · 2013
Typeletter
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of AlbertaYork UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsStatement (logic)Radiative transferEnvironmental scienceInterpretation (philosophy)Climate changeAbsorption (acoustics)Atmospheric sciencesClimate modelConstraint (computer-aided design)MeteorologyClimatologyPhysicsComputer scienceGeologyPolitical scienceLawMathematicsOptics

Abstract

fetched live from OpenAlex

Jacobson argues that our statement that "many climate models may overestimate warming by BC" has not been demonstrated. Jacobson challenges our results on the basis that we have misinterpreted some model results, omitted optical focusing under high relative humidity conditions and by involatile components, and because our measurements consist of only two locations over short atmospheric time periods. We address each of these arguments, acknowledging important issues and clarifying some misconceptions, and stand by our observations. We acknowledge that Jacobson identified one detail in our experimental technique that places an additional constraint on the interpretation of our observations and reduces somewhat the potential consequences of the stated implications.

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)0.002
metaresearch head score (Gemma)0.011
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.043
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0430.034
Insufficient payload (model declined to judge)0.0070.007

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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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
GenreCommentary

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

Citations44
Published2013
Admission routes1
Has abstractyes

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