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Record W3201161805 · doi:10.1111/rec.13562

Restoration of a Lake <scp>Ontario‐connected</scp> fen through invasive <i>Typha</i> removal

2021· article· en· W3201161805 on OpenAlexaboutno aff
Andie Graham, Bradley Mudrzynski, Eli L. Polzer, Douglas A. Wilcox

Bibliographic record

VenueRestoration Ecology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersState University of New YorkNational Fish and Wildlife FoundationNew York State Department of Environmental Conservation
KeywordsTyphaWetlandBiomass (ecology)Invasive speciesEcologyWater qualityEnvironmental scienceTypha angustifoliaPlant communityBiologyEcological succession

Abstract

fetched live from OpenAlex

Lake‐level regulation that began in 1960 eliminated large fluctuations of Lake Ontario water levels, altering coastal wetland plant communities. More than a half century later, the altered hydroperiod supports dense, monotypic stands of invasive cattail ( Typha angustifolia and Typha × glauca ), which have diminished overall plant community diversity. As a result, Lake Ontario coastal wetlands are less capable of providing many of their traditional ecological functions. One such wetland is Buttonwood Fen, a floating, lake‐connected peatland on Lake Ontario's southern shore near Rochester, NY. We implemented cattail‐control measures from 2016 to 2018 with the goal of decreasing live and dead cattail biomass and increasing cover of native fen taxa. Site manipulation included removal of dead cattail biomass, cutting new cattail growth when rhizome carbohydrate reserves were at their lowest, and hand‐wicking regrowth with herbicide in early fall. Results showed a decrease in live cattail stem density and cover and dead biomass cover, as well as an increase in cover of fen taxa. Although not a replicated study, our results suggest that removing dead cattail biomass and targeted treatment of live cattail stems via cutting and hand‐wicking with glyphosate can reduce cattail and improve site quality.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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