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Record W3022270545 · doi:10.1002/ppp.2047

Hot trends and impact in permafrost science

2020· article· en· W3022270545 on OpenAlexaff
Ylva Sjöberg, Matthias Siewert, Ashley Rudy, Michel Paquette, Frédéric Bouchard, Julie Malenfant‐Lepage, Michael Fritz

Bibliographic record

VenuePermafrost and Periglacial Processes · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalQueen's UniversityGovernment of Northwest Territories
Fundersnot available
KeywordsPermafrostClimate changeGlobal warmingClimate scienceWork (physics)OutreachBibliometricsScope (computer science)Physical geographyEarth scienceGeographyGeologyLibrary sciencePolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract An increased interest in Arctic environments, mainly due to climate change, has changed the conditions for permafrost research in recent years. This change has been accompanied by a global increase in scientific publications, as well as a trend towards open access publications. We have analyzed abstracts, titles and keywords for publications on permafrost from 1998 to 2017 to identify developments (topics, impact and collaboration) in the field of permafrost research in light of these changes. Furthermore, to understand how scientists build on and are inspired by each other's work, we have (a) developed citation networks from scientific publications on permafrost and (b) conducted an online survey on inspiration in permafrost science. Our results show an almost 400% increase in publications containing the word permafrost in the title, keywords or abstract over the study period, and a strong increase in climate‐change‐related research in terms of publications and citations. Survey respondents ( n = 122) find inspiration not only in scientific journal publications, but to a large extent in books and public outreach materials. We argue that this increase in global‐scope issues (i.e., climate change) complementing core permafrost research has provided new incentives for international collaborations and wider communication efforts.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.036
GPT teacher head0.278
Teacher spread0.242 · 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.

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

Citations31
Published2020
Admission routes1
Has abstractyes

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