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Record W4396932839 · doi:10.1111/cobi.14287

An ecologically sound and participatory monitoring network for pan‐Arctic seabirds

2024· article· en· W4396932839 on OpenAlexaff
Manon Clairbaux, Mia Rönkä, Tycho Anker‐Nilssen, Yuri Artukhin, Jóhannis Danielsen, Maria Gavrilo, Grant Gilchrist, Erpur Snær Hansen, April Hedd, Robert Kaler, Kathy J. Kuletz, Bergur Olsen, Mark L. Mallory, Flemming Ravn Merkel, Hallvard Strøm, Jérôme Fort, David Grémillet

Bibliographic record

VenueConservation Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsAcadia UniversityEnvironment and Climate Change Canada
FundersInstitut Polaire Français Paul Emile VictorAgence Nationale de la RechercheBelmont Forum
KeywordsCircumpolar starRepresentativeness heuristicSeabirdArcticGeographyEnvironmental monitoringEnvironmental resource managementBaseline (sea)EcologyEnvironmental scienceFisheryOceanographyBiology

Abstract

fetched live from OpenAlex

In a warming Arctic, circumpolar long-term monitoring programs are key to advancing ecological knowledge and informing environmental policies. Calls for better involvement of Arctic peoples in all stages of the monitoring process are widespread, although such transformation of Arctic science is still in its infancy. Seabirds stand out as ecological sentinels of environmental changes, and priority has been given to implement the Circumpolar Seabird Monitoring Plan (CSMP). We assessed the representativeness of a pan-Arctic seabird monitoring network focused on the black-legged kittiwake (Rissa tridactyla) by comparing the distribution of environmental variables for all known versus monitored colonies. We found that with respect to its spatiotemporal coverage, this monitoring network does not fully embrace current and future environmental gradients. To improve the current scheme, we designed a method to identify colonies whose inclusion in the monitoring network will improve its ecological representativeness, limit logistical constraints, and improve involvement of Arctic peoples. We thereby highlight that inclusion of study sites in the Bering Sea, Siberia, western Russia, northern Norway, and southeastern Greenland could improve the current monitoring network and that their proximity to local populations might allow increased involvement of local communities. Our framework can be applied to improve existing monitoring networks in other ecoregions and sociological contexts.

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 score0.077
Threshold uncertainty score0.998

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)0.0030.000

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 head0.111
GPT teacher head0.342
Teacher spread0.231 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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