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Scientists' warning of threats to mountains

2022· review· en· W4295013546 on OpenAlexfundno aff
Dirk S. Schmeller, Davnah Urbach, Kieran A. Bates, Jordi Catalán, Dan Cogălniceanu, Matthew C. Fisher, Jan Friesen, Leopold Füreder, Veronika Gaube, Marilen Haver, Dean Jacobsen, Gaël Le Roux, Y Lin, Adeline Loyau, Oliver Machate, Andreas Mayer, Ignacio Palomo, Christoph Plutzar, Hugo Sentenac, Rubén Sommaruga, Rocco Tiberti, William J. Ripple

Bibliographic record

VenueThe Science of The Total Environment · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersNatural Environment Research CouncilCanadian Institute for Advanced ResearchUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiAgricultural Research InstituteAustrian Science FundAgence Nationale de la RechercheSight Research UKBiodiversa+Conchologists of AmericaMedical Research CouncilMinisterul Cercetării, Inovării şi DigitalizăriiJoint Programming Initiative Water challenges for a changing worldHsinchu Science Park Bureau, Ministry of Science and Technology, TaiwanAXA Research Fund
KeywordsBiodiversityEcosystemEcosystem healthClimate changeEcosystem servicesHabitat destructionEcologyOverexploitationHabitatEnvironmental resource managementGeographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0030.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0500.001

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 head0.060
GPT teacher head0.298
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations124
Published2022
Admission routes1
Has abstractno

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