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Record W1992401847 · doi:10.1063/1.4821418

The international surface temperature initiative

2013· article· en· W1992401847 on OpenAlexaff
Peter Thorne, J. H. Lawrimore, Kate M. Willett, Rob Allan, Richard E. Chandler, A. Mhanda, Michael de Podesta, Antonio Possolo, J. V. Revadekar, Matilde Rusticucci, Peter A. Stott, Geoffroy F. Strouse, Blair Trewin, X. L. Wang, Akiyo Yatagai, Christopher J. Merchant, Andrea Merlone, T. C. Peterson, E. Marian Scott

Bibliographic record

VenueAIP conference proceedings · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric AdministrationMet Office
KeywordsBenchmarkingBenchmark (surveying)Raw dataComputer scienceDigitizationProcess (computing)Data scienceInstrumentation (computer programming)Data miningDatabaseRemote sensingGeographyTelecommunications

Abstract

fetched live from OpenAlex

The aim of International Surface Temperature Initiative is to create an end-to-end process for analysis of air temperature data taken over the land surface of the Earth. The foundation of any analysis is the source data. Land surface air temperature records have traditionally been stored in local, organizational, national and international holdings, some of which have been available digitally but many of which are available solely on paper or as imaged files. Further, economic and geopolitical realities have often precluded open sharing of these data. The necessary first step therefore is to collate readily available holdings and augment these over time either through gaining access to previously unavailable digital data or through data rescue and digitization activities. Next, it must be recognized that these historical measurements were made primarily in support of real-time weather applications where timeliness and coverage are key. At almost every long-term station it is virtually certain that changes in instrumentation, siting or observing practices have occurred. Because none of the historical measures were made in a metrologically traceable manner there is no unambiguous way to retrieve the true climate evolution from the heterogeneous raw data holdings. Therefore it is desirable for multiple independent groups to produce adjusted data sets (so-called homogenized data) to adequately understand the data characteristics and estimate uncertainties. Then it is necessary to benchmark the performance of the contributed algorithms (equivalent to metrological software validation) through development of realistic benchmark datasets. In support of this, a series of successive benchmarking and assessment cycles are envisaged, allowing continual improvement while avoiding over-tuning of algorithms. Finally, a portal is proposed giving access to related data-products, utilizing the assessment results to provide guidance to end-users on which product is the most suited to their needs. Recognizing that the expertise of the metrological community has been under-utilized historically in such climate data analysis problems, the governance of the Initiative includes significant representation from the metrological community. We actively welcome contributions from interested parties to any relevant aspects of the Initiative work.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2013
Admission routes1
Has abstractyes

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