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Record W2897146434 · doi:10.1080/14634988.2018.1536437

Habitat rehabilitation in the Detroit River Area of Concern

2018· article· en· W2897146434 on OpenAlexaffabout
John H. Hartig, Christian J. Sanders, R. J. H. Wyma, James C. Boase, Edward F. Roseman

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsToronto and Region Conservation AuthorityBalsillie School of International Affairs
Fundersnot available
KeywordsHabitatWildlifeGeographyRestoration ecologyEnvironmental resource managementEnvironmental planningWatershed managementShoreOutreachEnvironmental protectionFisheryWatershedEcologyEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

Loss and degradation of fish and wildlife habitat is a long-standing issue in the Detroit River. The Detroit River Remedial Action Plan helped agencies and stakeholder groups reach agreement on impaired beneficial uses, including loss of fish and wildlife habitat, and helped mobilize all stakeholders to rehabilitate habitat. Many organizations played key roles, including the Detroit River Public Advisory Council, Detroit River Canadian Cleanup, State of the Strait Conferences, American and Canadian Heritage River Initiatives, Detroit River International Wildlife Refuge, and Western Lake Erie Watersheds Priority Natural Area. Accomplishments include: 14 habitat restoration projects on both the Canadian and U.S. side of the Detroit River; 53 soft shoreline engineering projects in the watershed; nine fish spawning reefs in the river, Common Tern habitat in four locations; and many wetland and green infrastructure projects. Based on Detroit River habitat restoration efforts over the last 32 years, the following advice is offered: reach agreement on severity and geographic extent of the problem; practice adaptive management; involve habitat experts up front in project design; establish quantitative targets for project success; ensure sound multidisciplinary technical support; start with demonstration projects; treat habitat projects as experiments; involve citizen scientists in monitoring; measure benefits; communicate and celebrate successes; promote education and outreach.

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)0.002
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 score0.203
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.001

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 head0.017
GPT teacher head0.262
Teacher spread0.244 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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