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Record W1973060918 · doi:10.1371/journal.pone.0118928

Vigilance and Activity Time-Budget Adjustments of Wintering Hooded Cranes, Grus monacha, in Human-Dominated Foraging Habitats

2015· article· en· W1973060918 on OpenAlexaff
Chunlin Li, Lizhi Zhou, Xu Li, Niannian Zhao, Guy Beauchamp

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversité de Montréal
FundersNatural Science Foundation of Anhui ProvinceAnhui Postdoctoral Science FoundationMinistry of Education of the People's Republic of China
KeywordsForagingHabitatWetlandVigilance (psychology)WaterfowlEcologyFlockFloodplainGeographyHuman–wildlife conflictHabitat destructionGrus (genus)FisheryWildlifeBiology

Abstract

fetched live from OpenAlex

Due to loss and degradation of natural wetlands, waterbirds increasingly rely on surrounding human-dominated habitats to obtain food. Quantifying vigilance patterns, investigating the trade-off among various activities, and examining the underlying mechanisms will help us understand how waterbirds adapt to human-caused disturbances. During two successive winters (November-February of 2012-13 and 2013-14), we studied the hooded crane, Grus monacha, in the Shengjin Lake National Nature Reserve (NNR), China, to investigate how the species responds to human disturbances through vigilance and activity time-budget adjustments. Our results showed striking differences in the behavior of the cranes when foraging in the highly disturbed rice paddy fields found in the buffer zone compared with the degraded natural wetlands in the core area of the NNR. Time spent vigilant decreased with flock size and cranes spent more time vigilant in the human-dominated buffer zone. In the rice paddy fields, the birds were more vigilant but also fed more at the expense of locomotion and maintenance activities. Adult cranes spent more time vigilant and foraged less than juveniles. We recommend habitat recovery in natural wetlands and community co-management in the surrounding human-dominated landscape for conservation of the hooded crane and, generally, for the vast numbers of migratory waterbirds wintering in the middle and lower reaches of the Yangtze River floodplain.

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 score0.193
Threshold uncertainty score0.407

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 head0.038
GPT teacher head0.246
Teacher spread0.207 · 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

Citations51
Published2015
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicAvian ecology and behaviorFrench-language works237,207