{
  "abstract": "Camera traps generate massive volumes of data, yet their decision-support potential is diluted by the bottleneck of manual processing. Automated classification has largely solved this, but classification alone does not remove the barriers to adoption: it must be coupled with the means to turn predictions into a verified, shareable dataset, which no existing tool does end-to-end, offline, and without programming. The shift from a familiar manual workflow requires trusting new, often far less familiar, technology, and that apprehension is itself a barrier. One dichotomy compounds this barrier: camera-trap projects are increasingly delivered by teams (research groups, citizen-science projects), yet existing solutions prioritise either distribution (web-based platforms) or speed (local applications for bulk processing). Addressing these core requirements will broaden access to AI-integrated camera-trap workflows and reduce the apprehension that deters adoption. Specifically, a tool that operates offline to keep data under the user's control, processes data efficiently in bulk, supports structured and distribution-ready validation with expert judgement in the loop, and produces interoperable, transparent, and shareable outputs would build the trust needed to drive adoption. wildtag.ai integrates these competing priorities into a single, coherent workflow. Rather than trading distribution against speed, it treats camera-trap analysis as a managed, multi-contributor project. It is a free, openly available desktop application that runs entirely offline and includes automated detection and species classification, efficient and distribution-ready validation, and export to the Camtrap Data Package (Camtrap DP) standard. It builds on a curated selection of established models, bundled and hosted locally through a small registry, so that no coding, cloud service, or internet connection is required. Solution: wildtag.ai offers the user community a tool to turn backlogs of images into verified, shareable data without specialist infrastructure, subscriptions, or loss of data control. This makes an ambitious camera-trap programme tractable for a small team or volunteer network. It is built to support the wider community: giving model developers an open route to reach practitioners, letting projects of any scale adopt a consistent workflow, and helping to democratise knowledge discovery by dismantling the cost, infrastructure, and expertise barriers that put AI-supported analysis out of reach.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Chris Sutherland"
    },
    {
      "name": "Oliver Hartley"
    },
    {
      "name": "Alessandro Araldi"
    },
    {
      "name": "Deon Roos"
    },
    {
      "name": "Xavier Lambin"
    },
    {
      "name": "Amber Cowans"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cn8tjwf4sdvr6.bib",
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  "doi": "10.32942/x2ch53",
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  "licence_label": "CC BY 4.0",
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  "native_subjects": [
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      "bepress",
      "Artificial Intelligence and Robotics"
    ],
    [
      "bepress",
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    ],
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      "bepress",
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      "bepress",
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    ],
    [
      "bepress",
      "Environmental Monitoring"
    ],
    [
      "bepress",
      "Environmental Sciences"
    ],
    [
      "bepress",
      "Image processing"
    ],
    [
      "bepress",
      "Life Sciences"
    ],
    [
      "bepress",
      "Monitoring"
    ],
    [
      "bepress",
      "Natural Resources Management and Policy"
    ],
    [
      "bepress",
      "Natural Resources and Conservation"
    ],
    [
      "bepress",
      "Physical Sciences and Mathematics"
    ],
    [
      "bepress",
      "Sensor"
    ],
    [
      "bepress",
      "Terrestrial and Aquatic Ecology"
    ],
    [
      "bepress",
      "Wildlife"
    ],
    [
      "bepress",
      "Workflow"
    ],
    [
      "openalex",
      "2303|Wildlife Ecology and Conservation"
    ]
  ],
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  "pdf_url": "https://ecoevorxiv.org/repository/object/14737/download/27289/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cn8tjwf4sdvr6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "14737",
      "source": "ecoevorxiv",
      "url": "https://ecoevorxiv.org/repository/view/14737/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Bioinformatics",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "bepress:Ecology and Evolutionary Biology",
      "subfield": "ecology"
    },
    {
      "field": "biology",
      "origin": "bepress:Ecology and Evolutionary Biology",
      "subfield": "evolutionary-biology"
    },
    {
      "field": "earth",
      "origin": "bepress:Environmental Monitoring",
      "subfield": "environmental-science"
    }
  ],
  "title": "wildtag.ai: an open-access end-to-end offline AI-assisted desktop application for managing camera trap projects",
  "title_text": "wildtag.ai: an open-access end-to-end offline AI-assisted desktop application for managing camera trap projects",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/cn8tjwf4sdvr6",
  "venue": "EcoEvoRxiv",
  "versions": []
}