A survey of published research, from the Essentia Memoria newsroom. It lists real papers, each looked up at its DOI registry; the game has not repeated their results.
Research survey
What AI research has found: watching wild animals
Happened: · Published:
12 published works on watching wild animals, each looked up at its DOI registry before it was listed here, with what each one reports in plain words.
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News from the Essentia Memoria game universe. What AI research has found: watching wild animals. 12 published works on watching wild animals, each looked up at its DOI registry before it was listed here, with what each one reports in plain words. This survey lists what each paper reports. The game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited. This report was written from the world record by a fixed template. The full story and its source records are on essentiamemoria.com.
The question
Can a model count and describe the animals that walk past a hidden camera?
Claims, and where each one comes from
- Where this research lives in the game (a place the world grammar builds)wildlife-park → zoo
Game simulation · Set in code
src/lib/world-grammar/place-grammar.ts · resolvePurpose('wildlife-park') - Published works in this survey, each resolved at its DOI registry12
Real-world knowledge · Source [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]
- A network named, counted and described the animals in millions of camera-trap photos from the Serengeti.Norouzzadeh 2018
Real-world knowledge · Source [1]
- Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife MonitoringNguyen 2017
Real-world knowledge · Source [2]
- Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networksGomez-Villa 2017
Real-world knowledge · Source [3]
- Identifying animal species in camera trap images using deep learning and citizen scienceWilli 2018
Real-world knowledge · Source [4]
- Machine learning for image based species identificationWäldchen 2018
Real-world knowledge · Source [5]
- Machine learning to classify animal species in camera trap images: Applications in ecologyTabak 2018
Real-world knowledge · Source [6]
- A deep active learning system for species identification and counting in camera trap imagesNorouzzadeh 2020
Real-world knowledge · Source [7]
- Real-time bioacoustics monitoring and automated species identificationAide 2013
Real-world knowledge · Source [8]
- Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and ConservationGonzalez 2016
Real-world knowledge · Source [9]
- Computational bioacoustics with deep learning: a review and roadmapStowell 2022
Real-world knowledge · Source [10]
- Perspectives in machine learning for wildlife conservationTuia 2022
Real-world knowledge · Source [11]
- Deep learning-based appearance features extraction for automated carp species identificationBanan 2020
Real-world knowledge · Source [12]
References, checked at their registry
[1] Norouzzadeh (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1719367115
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning” (2018)
[2] Nguyen (2017). Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring. IEEE International Conference on Data Science and Advanced Analytics (DSAA). doi:10.1109/dsaa.2017.31
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring” (2017)
[3] Gomez-Villa (2017). Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks. Ecological Informatics. doi:10.1016/j.ecoinf.2017.07.004
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks” (2017)
[4] Willi (2018). Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13099
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Identifying animal species in camera trap images using deep learning and citizen science” (2019)
[5] Wäldchen (2018). Machine learning for image based species identification. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13075
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Machine learning for image based species identification” (2018)
[6] Tabak (2018). Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13120
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Machine learning to classify animal species in camera trap images: Applications in ecology” (2019)
[7] Norouzzadeh (2020). A deep active learning system for species identification and counting in camera trap images. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13504
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “A deep active learning system for species identification and counting in camera trap images” (2021)
[8] Aide (2013). Real-time bioacoustics monitoring and automated species identification. PeerJ. doi:10.7717/peerj.103
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Real-time bioacoustics monitoring and automated species identification” (2013)
[9] Gonzalez (2016). Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors. doi:10.3390/s16010097
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation” (2016)
[10] Stowell (2022). Computational bioacoustics with deep learning: a review and roadmap. PeerJ. doi:10.7717/peerj.13152
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Computational bioacoustics with deep learning: a review and roadmap” (2022)
[11] Tuia (2022). Perspectives in machine learning for wildlife conservation. Nature Communications. doi:10.1038/s41467-022-27980-y
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Perspectives in machine learning for wildlife conservation” (2022)
[12] Banan (2020). Deep learning-based appearance features extraction for automated carp species identification. Aquacultural Engineering. doi:10.1016/j.aquaeng.2020.102053
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:26 PM UTC · the registry holds “Deep learning-based appearance features extraction for automated carp species identification” (2020)
Review
Not peer reviewed. No human scientist has reviewed this survey. Its template and landmark summaries were written with an AI assistant (Claude); the papers it lists are real and each was checked at its registry, so anyone can follow the links and read them.
- Each listed DOI was resolved, and its title, year and first author matched the listing.
- Each work the search found is held by Crossref, a registry independent of the search.
- Each number in the headline and summary is accounted for by a listed claim.
- No candidate work was dropped: every one the desk found or named was confirmed by its registry.
Code version: deb216f03447
What the record does not say
This survey lists what each paper reports; the game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited.
How this was written
Landmark works named by the desk, then the most-cited works with a DOI that OpenAlex returns when titles and abstracts are searched for ("camera trap" OR wildlife OR "wild animal" OR "animal detection" OR "species identification" OR bioacoustic) AND ("machine learning" OR "deep learning" OR "neural network" OR "neural networks" OR "artificial intelligence" OR convolutional OR "reinforcement learning" OR "language model" OR transformer); a hit is kept only if its own title names the field and its title or abstract names an AI method, as whole words, and the hits that name the most of these words in their title come first. Each candidate was looked up at Crossref (OpenAlex as second resolver) and its title, year and first author compared; a work the search found is shown only when Crossref holds it. Landmark summaries were written by the desk; the other works are listed by their own titles.
Source records: code:src/lib/world-grammar/place-grammar.ts#resolvePurpose · doi:10.1073/pnas.1719367115 · doi:10.1109/dsaa.2017.31 · doi:10.1016/j.ecoinf.2017.07.004 · doi:10.1111/2041-210x.13099 · doi:10.1111/2041-210x.13075 · doi:10.1111/2041-210x.13120 · doi:10.1111/2041-210x.13504 · doi:10.7717/peerj.103 · doi:10.3390/s16010097 · doi:10.7717/peerj.13152 · doi:10.1038/s41467-022-27980-y · doi:10.1016/j.aquaeng.2020.102053