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Record W4293047397 · doi:10.1093/ornithology/ukac037

2022 AOS Ralph W. Schreiber Conservation Award to David Ainley and to Lindsay Young and Eric VanderWerf

2022· article· en· W4293047397 on OpenAlexaff
Marty L. Leonard, Márk E. Hauber, Helen F. James, Tony D. Williams, Karen L. Wiebe

Bibliographic record

VenueThe Auk · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsUniversity of SaskatchewanSimon Fraser UniversityDalhousie University
Fundersnot available
KeywordsEnvironmental ethicsPhilosophyArt

Abstract

fetched live from OpenAlex

David Ainley (left) Eric VanderWerf and (right) Lindsay Young The Ralph W. Schreiber Conservation Award is an AOS senior professional award that honors extraordinary conservation-related scientific contributions by an individual or small team. The award is named for Ralph Schreiber, a prominent figure in American ornithology known for his enthusiasm, energy, and dedication to research and conservation, particularly of seabirds. This award consists of an original piece of avian art, framed certificate, and an honorarium. In 2022, AOS is presenting two separate Ralph W. Schreiber Conservation Awards; one to David Ainley, and one to Lindsay Young and Eric VanderWerf. David Ainley is a Senior Ecologist with HT Harvey & Associates and Point Blue Conservation Science. Dr. Ainley is a prominent seabird biologist, having worked on marine ecosystems for more than 40 years. His research foci include the Farallon Islands’ ecosystem and marine ornithology in California, as well as his long-term studies of Adelie Penguin foraging and breeding biology in Antarctica. His work has uncovered the genetic impacts of calving icebergs on the population structure of penguin colonies and the competitive interaction of krill-foraging specialists in Antarctic waters, all aspects that are severely impacted by global change. In addition to his productive research career, Dr. Ainley has worked tirelessly to conserve marine organisms. He led the restoration of the Farallon Islands, removing tons of debris as well as feral animals, leading directly to the return of two species of breeding bird after a 100-year absence, and increasing the populations of others. Dr. Ainly also initiated efforts to designate the Ross Sea Marine Protected Area through numerous papers, presentations and film. In short, Dr. Ainley has been instrumental in advancing our knowledge of, and preservation of, marine birds and other organisms. The second Schreiber Conservation Award goes to Drs. Lindsay Young and Eric VanderWerf of Pacific Rim Conservation. Drs. Young and VanderWerf are being recognized for the sustained success of their conservation actions combined with their publication, individually and together, of a significant body of research on bird conservation and the biology of birds. Their long-term study of Hawaiian seabirds and land birds, combined with planning and execution of effective conservation actions, have helped to protect vulnerable breeding birds in Hawaiʻi. Conservation projects led by Pacific Rim Conservation encompass a range of techniques including acoustic survey and population monitoring, habitat restoration, chick fostering and translocation, social attraction, predator-proof fencing, and predator eradication. Their diverse conservation projects on multiple islands have reduced predation on, and improved habitat for, multiple species of breeding Hawaiian birds, and established new breeding colonies of several vulnerable seabird species. Drs. Young and VanderWerf are the authors of multiple scientific articles, book chapters and reports, and co-authors of the recent book, Conservation of Marine Birds (July 2022; Elsevier), on the factors influencing seabird conservation. The American Ornithological Society is honored to bestow the 2022 AOS Ralph W. Schreiber Conservation Awards to Dr. David Ainley and to Dr. Lindsay Young and Dr. Eric VanderWerf. Please check Wing Beat (https://americanornithology.org/blog/) for more detailed profiles of the two award winners.

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 score0.070
Threshold uncertainty score0.681

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 studies0.0010.000
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 head0.024
GPT teacher head0.224
Teacher spread0.200 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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