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Record W4221043789 · doi:10.5281/zenodo.6325814

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

2022· article· en· W4221043789 on OpenAlexaff
Timothy D. Meehan, Sarah P. Saunders, William V. DeLuca, Nicole L. Michel, Joanna Grand, Jill L. Deppe, Miguel F. Jimenez, Erika J. Knight, Nathaniel E. Seavy, Melanie Smith, Lotem Taylor, Chad Witko, Michael E. Akresh, David R. Barber, Erin M. Bayne, James C. Beasley, Jerrold L. Belant, Richard O. Bierregaard, Keith L. Bildstein, Than J. Boves, John N. Brzorad, Steven C. Campbell, Antonio Celis‐Murillo, Hilary A. Cooke, Robert Domenech, Laurie J. Goodrich, Elizabeth A. Gow, Aaron M. Haines, Michael T. Hallworth, Jason Hill, Amanda M. Holland, Scott Jennings, Roland Kays, D. Tommy King, Stuart A. Mackenzie, Peter P. Marra, Rebecca A. McCabe, Kent P. McFarland, Michael McGrady, Ron Melcer, D. Ryan Norris, Russell E. Norvell, Olin E. Rhodes, Christopher C. Rimmer, Amy L. Scarpignato, Adam Shreading, Jesse L. Watson, Chad B. Wilsey

Bibliographic record

VenueLincoln (University of Nebraska) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsBirds CanadaWildlife Conservation Society CanadaUniversity of GuelphUniversity of Alberta
Fundersnot available
KeywordsWestern hemisphereGeographySpatial ecologyBird migrationCartographyPhysical geographyBiologyEconomic geographyEcology

Abstract

fetched live from OpenAlex

Species-specific raster layers of the integrated index that combines eBird, band re-encounter, and tracking datasets using least-cost paths to illustrate spatial patterns of migration for 12 bird species during 2 migratory seasons (spring and fall). These raster layers are the product of the modeling framework developed in "Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere" published by Meehan et al. in Ecological Applications in 2022.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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