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Record W2995027907 · doi:10.1007/s13280-019-01300-z

Status and trends of circumpolar peregrine falcon and gyrfalcon populations

2019· article· en· W2995027907 on OpenAlexafffundabout
Alastair Franke, Knud Falk, Kevin Hawkshaw, Skip Ambrose, David L. Anderson, Peter J. Bente, Travis L. Booms, Kurt K. Burnham, Johan Ekenstedt, Ivan Fufachev, Sergey Ganusevich, Kenneth Johansen, Jeff Johnson, Sergey Kharitonov, Pertti Koskimies, Olga Kulikova, Peter Lindberg, Berth-Ove Lindström, William G. Mattox, Carol L. McIntyre, Svetlana Mechnikova, Dave Mossop, Søren Møller, Ólafur K. Nielsen, Tuomo Ollila, Arve Østlyngen, Ivan Pokrovsky, Kim G. Poole, Marco Restani, Bryce W. Robinson, Robert N. Rosenfield, Aleksandr Sokolov, Vasiliy Sokolov, Ted Swem, Katrin Vorkamp

Bibliographic record

VenueAMBIO · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsYukon UniversityAurora CollegeUniversity of Alberta
FundersEnvironment and Climate Change CanadaMiljøstyrelsen
KeywordsFalconCircumpolar starArcticGeographyArchipelagoEcologyEcosystemApex predatorEnvironmental resource managementBiologyOceanographyEnvironmental science

Abstract

fetched live from OpenAlex

The peregrine falcon (Falco peregrinus) and the gyrfalcon (Falco rusticolus) are top avian predators of Arctic ecosystems. Although existing monitoring efforts are well established for both species, collaboration of activities among Arctic scientists actively involved in research of large falcons in the Nearctic and Palearctic has been poorly coordinated. Here we provide the first overview of Arctic falcon monitoring sites, present trends for long-term occupancy and productivity, and summarize information describing abundance, distribution, phenology, and health of the two species. We summarize data for 24 falcon monitoring sites across the Arctic, and identify gaps in coverage for eastern Russia, the Arctic Archipelago of Canada, and East Greenland. Our results indicate that peregrine falcon and gyrfalcon populations are generally stable, and assuming that these patterns hold beyond the temporal and spatial extents of the monitoring sites, it is reasonable to suggest that breeding populations at broader scales are similarly stable. We have highlighted several challenges that preclude direct comparisons of Focal Ecosystem Components (FEC) attributes among monitoring sites, and we acknowledge that methodological problems cannot be corrected retrospectively, but could be accounted for in future monitoring. Despite these drawbacks, ample opportunity exists to establish a coordinated monitoring program for Arctic-nesting raptor species that supports CBMP goals.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.983

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)0.0180.000

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 head0.029
GPT teacher head0.261
Teacher spread0.232 · 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.

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

Citations23
Published2019
Admission routes3
Has abstractyes

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Same venueAMBIOSame topicSpecies Distribution and Climate ChangeFrench-language works237,207