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Record W2910991028

Effects of urban sprawl on sediment, surface water, and biota in the little Blackwater River, Blackwater National Wildlife Refuge, Dorchester County, Maryland

2018· dataset· en· W2910991028 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedataset
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBlackwaterWildlife refugeWetlandBrackish marshEnvironmental scienceWater qualityGeographyHydrology (agriculture)WatershedMarshSandhillRiparian zoneWildlifeEcologyHabitatGeology
DOInot available

Abstract

fetched live from OpenAlex

the little blackwater river drains a catchment area of approximately 11 229 hectares 27 748 acres about 516 hectares 1 276 acres in this watershed are currently developed for residential commercial and light industrial land use while the remaining 10 713 hectares 26 472 acres comprise a rural agricultural landscape consisting of cropland woodland forested and marshy riparian areas and tidal wetlands the land surface is level with frequent flooding due to poor drainage low flow gradients high water tables and hydric soils in recent years there has been extreme pressure to develop the little blackwater watershed the city of cambridge and dorchester county are considering zoning and permitting of nearly 3 238 hectares 8 000 acres for development in the next few years the little blackwater river drains directly into the blackwater national wildlife refuge the blackwater national wildlife refuge was established in 1933 as a refuge for migratory waterfowl the refuge includes more than 11 331 hectares 28 000 acres composed mainly of rich tidal marsh characterized by fluctuating water levels and variable salinity blackwater national wildlife refuge is one of the chief wintering areas for canada geese using the atlantic flyway geese number approximately 35 000 and ducks exceed 15 000 at the peak of fall migration usually in november urbanization of the little blackwater river could have an effect on water and sediment quality and may adversely affect fish and wildlife resources on blackwater national wildlife refuge we used the sediment quality triad approach to evaluate the potential impacts of the proposed development the sediment quality triad is a weight of evidence approach consisting of synoptically collected measures of sediment chemistry benthic fish community structure and sediment toxicity the study area included three sites upstream of the blackwater wildlife refuge on the little blackwater river three samples downstream on the refuge and a reference site on buttons creek an adjacent watershed that is comprised almost entirely of undeveloped forest and marsh in general the little blackwater river watershed has had little impact from the contaminants evaluated in this study although the benthic community in this watershed is depressed it is more likely a function of the physical characteristics of the shallow water system rather than an effect of contaminants in 2004 proposals were made to dorchester county for the development of two separate parcels of approximately 1 000 acres each within the little blackwater watershed nearly 4 500 homes and a golf course were planned for a 1 100 acre site along the north tributary and a mixed use industrial park was proposed near the east tributary southeast of cambridge by 2007 most of the plans were either scaled back or abandoned because developmental pressures on this watershed were greatly diminished in 2006 the second year data was similar to the first making this report a baseline study rather than a comparison of pre and post construction effects

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)0.002
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: Observational
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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