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General Features of the Arctic Relevant to Climate Change in Freshwater Ecosystems

2006· article· en· W2177044300 on OpenAlexaff
Terry D. Prowse, Frederick J. Wrona, James D. Reist, John E. Hobbie, Lucie Lévesque, Warwick F. Vincent

Bibliographic record

VenueAMBIO · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsImpactUniversity of VictoriaEnvironment and Climate Change Canada
Fundersnot available
KeywordsFreshwater ecosystemArcticLake ecosystemEnvironmental scienceEcologyBiotaRiver ecosystemPermafrostEcosystemClimate changeArctic ecologyAquatic ecosystemCircumpolar starOceanographyGeologyBiology

Abstract

fetched live from OpenAlex

Large variations exist in the size, abundance and biota of the two principal categories of freshwater ecosystems, lotic (flowing water; e.g., rivers, streams, deltas and estuaries) and lentic (standing water; lakes, ponds and wetlands) found across the circumpolar Arctic. Arctic climate, many components of which exhibit strong variations along latitudinal gradients, directly affects a range of physical, chemical and biological processes in these aquatic systems. Furthermore, arctic climate creates additional indirect ecological effects through the control of terrestrial hydrologic systems and processes, particularly those associated with cryospheric components such as permafrost, freshwater ice and snow accumulation/ablation. The ecological structure and function of arctic freshwater systems are also controlled by external processes and conditions, particularly those in the headwaters of the major arctic rivers and in the adjacent marine environment. The movement of physical, chemical and biotic components through the interlinked lentic and lotic freshwater systems are major determinants of arctic freshwater ecology.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.232
Teacher spread0.208 · 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 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

Citations77
Published2006
Admission routes1
Has abstractyes

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Same venueAMBIOSame topicClimate change and permafrostFrench-language works237,207