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Record W7135789067

Future Options I: Aquaculture, Hatcheries, Tourism, Transportation, and Local Initiatives

2007· book-chapter· en· W7135789067 on OpenAlexaboutno aff
Shelby E. Temple

Bibliographic record

VenueExplore Bristol Research · 2007
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsResource (disambiguation)Natural resourceCoastal erosionNatural (archaeology)Investment (military)Coastal managementEcosystemProductivity
DOInot available

Abstract

fetched live from OpenAlex

While coasts are often places of unsurpassing beauty, many coastal communities suffer from poverty, unemployment, health risks, and the effects of environmental degradation. Coasts Under Stress is a unique interdisciplinary exploration of the complex interplay of economy, culture, environment, and health in the coastal communities of eastern and western Canada. Rosemary Ommer and her project team combine formal scientific (natural and social) and humanist analysis with an examination of the lived experience of coastal people. They analyze community erosion created by economic decline and the ecosystem damage caused by unrelenting industrial pressure on natural resources and look at the history of coastal communities, their resource bases, their economies, and the way the lives of people are embedded in their environments. Coasts Under Stress shows that many coastal people are determined to survive in the places they love and stresses the need for investment to encourage the recovery of coastal communities.

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)0.000
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: none
Teacher disagreement score0.067
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0670.012

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 head0.090
GPT teacher head0.324
Teacher spread0.235 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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