MétaCan
Menu
Back to cohort
Record W3139267256 · doi:10.1002/wlb3.01016

Influence of water level management on vegetation and bird use of restored wetlands in the Montezuma Wetlands Complex

2021· article· en· W3139267256 on OpenAlexaff
Edward B. Farley, Michael L. Schummer, Donald J. Leopold, John M. Coluccy, Douglas C. Tozer

Bibliographic record

VenueWildlife Biology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsBirds Canada
Fundersnot available
KeywordsWetlandWaterfowlAbundance (ecology)Drawdown (hydrology)Environmental scienceHabitatVegetation (pathology)EcologyPlant communityWater qualityHydrology (agriculture)GeographyEcological successionBiologyGroundwater

Abstract

fetched live from OpenAlex

Active water management of wetlands promotes seed and tuber production to feed migrating waterfowl, but few assessments exist to determine how management actions influence wetland structure, vegetation and bird response throughout the year. We identified effects of full water drawdown, partial water drawdown and passive wetlands (no active dewatering during the growing season) on plant communities and bird abundance in wetlands of the Montezuma Wetlands Complex, New York, May–October 2016–2018 and February–April 2017–2019. We detected few differences in the plant community during June, but during September we detected greater vegetative forage quality index for waterfowl, annual plant cover and seed density in full and partial drawdowns than passive wetlands. Bird abundance was greater in June–July in passive wetlands and greater in September–October in full drawdowns. During spring migration, duck densities were greater in full and partial drawdowns. Our results indicate that wetland managers should use a mix of full drawdowns and passive wetlands to provide habitat for the greatest diversity and number of birds throughout the year.

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 score0.017
Threshold uncertainty score0.247

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)0.0000.000

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 head0.041
GPT teacher head0.256
Teacher spread0.215 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueWildlife BiologySame topicPeatlands and Wetlands EcologyFrench-language works237,207