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Record W4402481882 · doi:10.1111/2041-210x.14407

Introducing a unique animal <scp>ID</scp> and digital life history museum for wildlife metadata

2024· article· en· W4402481882 on OpenAlexafffund
Martin Wikelski, Michael Quetting, John M. Bates, Tanya Berger‐Wolf, Gil Bohrer, Luca Börger, Taylor K. Chapple, Margaret C. Crofoot, Sarah C. Davidson, Dina K. N. Dechmann, Diego Ellis‐Soto, Elizabeth R. Ellwood, Wolfgang Fiedler, Andrea Flack, Barbara Fruth, Novella Franconi, Rasmus Worsøe Havmøller, Julian Hirt, Nigel E. Hussey, Fabiola Iannarilli, Matthias Landwehr, Maximilian E. Müller, Thomas Mueller, U. Mueller, Ruth Y. Oliver, Jesko Partecke, Ivan Pokrovsky, Liya Pokrovskaya, Dustin R. Rubenstein, Christian Rutz, Kamran Safi, Andrea Santangeli, O. Louis van Schalkwyk, Ana M. M. Sequeira, Sherub Sherub, Tharmalingam Ramesh, Pauli Viljoen, Kaja A. Wasik, Timm A. Wild, Scott W. Yanco, Roland Kays

Bibliographic record

VenueMethods in Ecology and Evolution · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Windsor
FundersDivision of Environmental BiologyAustralian National UniversityDirectorate-General XII, Science, Research, and DevelopmentMax-Planck-GesellschaftSenckenberg Biodiversität und Klima ForschungszentrumOhio State UniversityUniversity of PretoriaBiological and Physical Sciences DivisionNational Geographic SocietyDirectorate for Biological SciencesSwansea UniversityUniversitetet i AgderNorth Carolina Museum of Natural SciencesUniversity of St AndrewsACT GovernmentUniversität KonstanzNorth Carolina State UniversityUniversity of WindsorOregon State UniversityNuclear Safety and Security CommissionGordon and Betty Moore FoundationH2020 Marie Skłodowska-Curie ActionsYale UniversityNational Aeronautics and Space Administration
KeywordsMetadataWildlifeWorld Wide WebComputer scienceGeographyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships. Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens. To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases. MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.060
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.284
Teacher spread0.261 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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