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Record W2966352862 · doi:10.1002/lol2.10116

Consequences of lake and river ice loss on cultural ecosystem services

2019· article· en· W2966352862 on OpenAlexafffund
Lesley B. Knoll, Sapna Sharma, Blaize A. Denfeld, Giovanna Flaim, Yukari Hori, John J. Magnuson, Dietmar Straile, Gesa A. Weyhenmeyer

Bibliographic record

VenueLimnology and Oceanography Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsThe Scarborough HospitalUniversity of TorontoYork University
FundersKempestiftelsernaGlobal Lake Ecological Observatory Network
KeywordsEcosystem servicesFishingEcosystemNorthern HemisphereEnvironmental scienceGeographyFisheryOceanographyPhysical geographyEnvironmental resource managementEcologyClimatologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract People extensively use lakes and rivers covered by seasonal ice. Although ice cover duration has been declining over the past 150 years for Northern Hemisphere freshwaters, we know relatively little about how ice loss directly affects humans. Here, we synthesize the cultural ecosystem services (i.e., services that provide intangible or nonmaterial benefits) and associated benefits supported by inland ice. We also provide, for the first time, empirical examples that give quantitative evidence for a winter warming effect on a wide range of ice‐related cultural ecosystem services and benefits. We show that in recent decades, warmer air temperatures delayed the opening date of winter ice roads and led to cancellations of spiritual ceremonies, outdoor ice skating races, and ice fishing tournaments. Additionally, our synthesis effort suggests unexploited data sets that allow for the use of integrative approaches to evaluate the interplay between inland ice loss and society.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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 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

Citations191
Published2019
Admission routes2
Has abstractyes

Explore more

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