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Record W2124733018 · doi:10.1002/ieam.1714

Opportunities and challenges of integrating ecological restoration into assessment and management of contaminated ecosystems

2015· article· en· W2124733018 on OpenAlexaff
Ruth N. Hull, Samuel N. Luoma, Bruce A. Bayne, John Iliff, Daniel J. Larkin, Mark W. Paschke, Sasha L. Victor, Sara E. Ward

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsIntrinsik (Canada)
FundersKorea Institute for Industrial Economics and TradeCommonwealth Scientific and Industrial Research OrganisationNewmont CorporationU.S. Department of the Interior
KeywordsProcess (computing)Restoration ecologyRemedial educationEnvironmental resource managementEnvironmental remediationEnvironmental restorationEcosystem servicesLiabilityAdaptive managementEnvironmental planningBusinessEcosystemProcess managementComputer scienceEnvironmental scienceEcologyPolitical scienceContamination

Abstract

fetched live from OpenAlex

Abstract Ecosystem restoration planning near the beginning of the site assessment and management process (“early integration”) involves consideration of restoration goals from the outset in developing solutions for contaminated ecosystems. There are limitations to integration that stem from institutional barriers, few successful precedents, and limited availability of guidance. Challenges occur in integrating expertise from various disciplines and multiple, sometimes divergent interests and goals. The more complex process can result in timing, capacity, communication, and collaboration challenges. On the other hand, integrating the 2 approaches presents new and creative opportunities. For example, integration allows early planning for expanding ecosystem services on or near contaminated lands or waters that might otherwise have been unaddressed by remediation alone. Integrated plans can explicitly pursue ecosystem services that have market value, which can add to funds for long-term monitoring and management. Early integration presents opportunities for improved and productive collaboration and coordination between ecosystem restoration and contaminant assessment and management. Examples exist where early integration facilitates liability resolution and generates positive public relations. Restoration planning and implementation before the completion of the contaminated site assessment, remediation, or management process (“early restoration”) can facilitate coordination with offsite restoration options and a regional approach to restoration of contaminated environments. Integration of performance monitoring, for both remedial and restoration actions, can save resources and expand the interpretive power of results. Early integration may aid experimentation, which may be more feasible on contaminated lands than in many other situations. The potential application of concepts and tools from adaptive management is discussed as a way of avoiding pitfalls and achieving benefits in early integration. In any case, there will be challenges with early integration of restoration concepts for contaminated ecosystems, but the benefits are likely to outweigh them. Integr Environ Assess Manag 2016;12:296–305. © 2015 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points There will be challenges with early integration of restoration concepts for contaminated ecosystems, but the benefits are likely to outweigh them. Integration allows early planning for, and expedited recovery of ecosystem services. Off-site options for restoration present opportunities for a collaborative, co-ordinated, regional-level approach to restoration of contaminated environments and could help with liability resolution, thereby generating positive public relations and leveraging new partners to expand scale. Structured decision making, specifically adaptive management involving recurrent decision making over time, is a useful tool as a way of avoiding pitfalls and achieving benefits in early integration.

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)0.001
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 score0.366
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.267
Teacher spread0.221 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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