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Record W4254640974 · doi:10.13031/aim.20141893216

Understanding disputes around environmental flows: the role of ecosystem services

2014· article· en· W4254640974 on OpenAlexaboutno aff
Kate Reilly, Jan Adamowski

Bibliographic record

Venue2014 ASABE Annual International Meeting · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesEcosystemEnvironmental resource managementRiparian zoneBiodiversityWetlandStakeholderEnvironmental scienceFreshwater ecosystemBusinessEnvironmental planningEcologyPolitical science

Abstract

fetched live from OpenAlex

<abstract> <bold>Abstract.</bold> The impacts of modification of river flows on aquatic and riparian ecology are widely acknowledged. For example, reduced flows caused by water abstraction or retention of water behind dams lead to reduced abundance and diversity of native fish species. The resulting ecosystem degradation is linked to deterioration of ecosystem services, such as fisheries, water purification, and aesthetic value. Many of Canada’s rivers have been modified by dams, abstractions, and inter-basin diversions, causing, for example, wetland loss, reduced biodiversity and poorer water quality in Lake Ontario and many other places. While methods of determining flows needed to maintain freshwater and floodplain ecosystems (“environmental flows”) have been extensively debated, progress in implementing the environmental flow requirements has been limited. Often this is due to stakeholder opposition arising from the perception that human and ecosystem requirements are in direct conflict. The concept of ecosystem services may be useful for emphasising the benefits of ecosystem protection for human wellbeing. Future research will analyse how environmental flow policies are perceived by a range of stakeholders, with particular attention to the value they give to the ecosystem services provided by the policy. In particular, how different stakeholders’ perceptions affect opposition to or support for the policy will be analysed, and potential common ground between opposing parties will be identified.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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 teacher head, 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
Published2014
Admission routes1
Has abstractyes

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