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Towards threshold-based management of freshwater ecosystems in the context of climate change

2014· article· en· W2006567969 on OpenAlexfundno aff
Junguo Liu, Giri Kattel, Hans Peter H. Arp, Hong Yang

Bibliographic record

VenueEcological Modelling · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesOrganization Department of the Central Committee of the Communist Party of ChinaUniversity of LeedsNational Natural Science Foundation of ChinaMcGill University
KeywordsClimate changeFreshwater ecosystemEcosystem servicesEcosystemEnvironmental resource managementContext (archaeology)Ecosystem managementEnvironmental scienceBusinessNovel ecosystemEnvironmental planningEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Climate change is an increasing threat to freshwater ecosystem goods and services. We review recent research regarding the direct and indirect impacts of climate change on freshwater ecosystems and the severity of their undesirable effects on ecosystem processes and services. Appropriate management strategies are needed to mitigate the long-term or irreversible losses of ecosystem services caused by climate change. To address this, this review puts forward a threshold-based management framework as a potential platform for scientists, decision makers and stakeholders of freshwater ecosystems to work together in reducing risks from climate change. In this framework, the susceptibility of local freshwater ecosystems to change beyond thresholds is continuously investigated and updated by scientists, used to design policy targets by decision makers, and used to establish mitigation measures by local stakeholders.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.232
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations50
Published2014
Admission routes1
Has abstractyes

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