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Record W3081933617 · doi:10.1098/rsta.2020.0266

The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning

2020· article· en· W3081933617 on OpenAlexaff
Martin Solan, Philippe Archambault, Paul E. Renaud, Christian März

Bibliographic record

VenuePhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversité LavalArcticNet
FundersNatural Environment Research CouncilSight Research UK
KeywordsBiogeochemical cycleEcosystemArcticEnvironmental scienceOceanographyThe arcticEcologyEnvironmental resource managementGeologyBiology

Abstract

fetched live from OpenAlex

The Arctic region is undergoing some of the most rapid rates of climate change in the world [1], with dramatic transformations underway in terrestrial, coastal and offshore environments that have immediate and long-term consequences for socio-ecological systems (e.g. [2–5]). Significant changes in the type, extent and thickness of ice cover [6], meltwater input [7] and water mass dynamics [8], coupled with warming and ocean acidification [9], have already begun to impact ecosystem processes and the flora and fauna that inhabit a range of Arctic habitats [10]. The pace of change is such that our understanding of the way in which Arctic systems are structured and function is outdated, and insufficient to inform management, mitigation and adaptation efforts across the region [11,12]. Projections indicate that, even if global stabilization of temperature below 1.5°C is realized, changes will continue to manifest over an extended period, perhaps even millennial timescales [13] and may include unprecedented shifts in structure [14]. Changes to key components of Arctic ecosystems are already occurring, yet the collated evidence of how changes to baseline conditions are proceeding across the Arctic Ocean is still poorly constrained [15], focused on a limited number of exemplar areas [16], and seldom adopts a holistic view that begins to provide a nuanced understanding of the modus operandi of the Arctic [17]. This is concerning because informed decision- and policy-making benefits from a broad understanding of system dynamics, including feedbacks and the likelihood of ecological surprises [18], yet the focus of study is already shifting from the natural sciences to social sciences and humanities to meet legislative and policy demands [19]. Now more than ever, foundational concepts and evidence are needed to support sustainable management and policy, preferably with a focus on continually acquiring, interpreting and applying new interdisciplinary knowledge to enhance understanding [20].

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.216
Teacher spread0.187 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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