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Record W1574313663 · doi:10.22230/jem.2008v9n1a380

Blue ecology and climate change

2008· article· en· W1574313663 on OpenAlexaff
Michael D. Blackstock

Bibliographic record

VenueJournal of Ecosystems and Management · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicScience and Climate Studies
Canadian institutionsKamloops Art Gallery
Fundersnot available
KeywordsClimate changeMeltwaterPermafrostContext (archaeology)EcologyAcknowledgementEnvironmental scienceArcticEcosystemGeographyPhysical geographyGlacierBiology

Abstract

fetched live from OpenAlex

Building on the concept of “Blue Ecology” introduced in previous BC Journal of Ecosystems and Management articles, the author proposes that we re-examine climate change from this “water first” angle: What is happening to the world's water in the context of climate change? As evidenced by higher temperatures resulting in melting Arctic ice, melting permafrost, freshwater (i.e., cold meltwater) influx into oceans, shifting water currents, drought, water stress, higher rainfall, and floods, the rhythm of water's transformations between solid, liquid, and gaseous states on our planet is undergoing a significant change, and at a significant rate. The author sees as essential the acknowledgement of water's central functional and spiritual roles in our world, and urges us to apply both respect and science-based understanding as we develop collaborative climate change mitigation strategies and instill this respect and understanding in younger generations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.031

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 studies0.0050.015
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.028
GPT teacher head0.235
Teacher spread0.207 · 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
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

Citations2
Published2008
Admission routes1
Has abstractyes

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