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Record W4361270084 · doi:10.21203/rs.3.rs-2741673/v1

Spring migration patterns of red knots in the Southeast United States disentangled using automated telemetry

2023· preprint· en· W4361270084 on OpenAlexaff
Adam Smith, Felicia J. Sanders, Kara L. Lefevre, Janet M. Thibault, Kevin S. Kalasz, Maina Handmaker, Fletcher M. Smith, Tim Keyes

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsBayFlywayGeographyStructural basinOceanographyPopulationFisheryArcticCircumpolar starPhysical geographyEcologyGeologyArchaeologyPaleontologyHabitatBiology

Abstract

fetched live from OpenAlex

Abstract Background and Methods Shorebirds evolved flexibility in their migration strategies, with demonstrated variation even within the same population. Research in the last decade revealed diverse migration routes for red knots using the Western Atlantic flyway. Red Knots use the Southeast United States (North and South Carolina, Georgia, and Florida) as a stopover during north and southbound migration and during the winter. We examined northbound red knot migration routes and timing from the Southeast United States using an automated telemetry network. Our primary goal was to evaluate the relative use of an Atlantic migratory route through Delaware Bayversus an inland route through the Great Lakes en route to Arctic breeding grounds and to identify areas of apparent stopovers. Secondarily, we explored the association of red knot routes and ground speeds with prevailing atmospheric conditions. Results Most Red Knots migrating north from the Southeast United States skipped or likely skipped Delaware Bay (73%) while 27% of the knots stopped in Delaware Bay for at least 1 day. Most birds that skipped Delaware Bay traveled north, through the eastern Great Lake Basin. A few knots used an Atlantic Coast strategy that did not include Delaware Bay, relying instead on the areas around Chesapeake Bay or New York Bay for stopovers. We did not detect stopovers in the Great Lakes Basin suggesting that knots move quickly through this region. Nearly half of the red knots were detected in either James Bay or Hudson Bay with the first day of detection ranging from 19 May to 7 June. Nearly 80% of migratory trajectories were associated with tailwinds at departure. Conclusions Most knots tracked in our study did not stop in the Great Lakes, thus making the Southeast United States the last terminal stopover for some knots before reaching Arctic habitats. This study demonstrates the diversity of red knot spring migration routes and underscores how critically important the Southeast United States is as a spring stopover site for red knots. Future conservation planning must include the full network of sites that support the varied migratory routes and strategies used by this declining shorebird species.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.091
GPT teacher head0.389
Teacher spread0.298 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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