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https://github.com/zoo-apps/papers.git
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zeekayandHanzo Dev
6658236499
Leadership and team cards, board and founding prose, an AI persona modelled on a named individual, contributor and partner entries, and personal correspondence addresses all named one person. The organisation carries them now. Other people keep their names. Where a card, a board list or a founding line named someone else alongside, they stay exactly as they were; only the one entry was removed, and prose that listed two names now reads with one. Sample and fixture data uses the placeholder name these tests already use elsewhere, so the IAM username defect the notes describe still reads as written. The zoo/yapp MIT notice names the foundation that publishes it. Co-authored-by: Hanzo Dev <[email protected]>
111 lines
4.7 KiB
TeX
111 lines
4.7 KiB
TeX
\documentclass[11pt,a4paper]{article}
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\usepackage{shared/zoocover}
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% ── Packages ──────────────────────────────────────────────────────────────────
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{lmodern}
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% geometry loaded by zoocover
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\usepackage{amsmath,amssymb,amsfonts}
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\usepackage{graphicx}
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\usepackage{booktabs}
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\usepackage{hyperref}
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\usepackage{cleveref}
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\usepackage{natbib}
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\usepackage{algorithm}
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\usepackage{algorithmic}
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\usepackage{xcolor}
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\usepackage{tikz}
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\usetikzlibrary{arrows.meta,positioning,shapes.geometric,fit}
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\usepackage{float}
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\usepackage{subcaption}
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\usepackage{enumitem}
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\usepackage{microtype}
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\usepackage{fancyhdr}
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\usepackage{abstract}
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\usepackage{multirow}
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% ── Page Style ────────────────────────────────────────────────────────────────
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\pagestyle{fancy}
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\fancyhf{}
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\fancyhead[L]{\small Zoo Labs Foundation Research}
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\fancyhead[R]{\small Citizen Science Platform}
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\fancyfoot[C]{\thepage}
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\renewcommand{\headrulewidth}{0.4pt}
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% ── Metadata ──────────────────────────────────────────────────────────────────
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\title{%
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\textbf{Citizen Science Platform: Gamified\\Biodiversity Data Collection}\\[0.5em]
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\large Zoo Labs Foundation Paper ZOO-CSP-2026
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}
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\author{
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Zoo Labs Foundation Research\\
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\texttt{[email protected]}\\[0.5em]
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\small With contributions from the Zoo Open Science Network
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}
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\date{September 2023}
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% ── Macros ────────────────────────────────────────────────────────────────────
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\newcommand{\R}{\mathbb{R}}
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\newcommand{\E}{\mathbb{E}}
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\newcommand{\Loss}{\mathcal{L}}
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\begin{document}
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\zoocoverpage
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% ── Abstract ──────────────────────────────────────────────────────────────────
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\begin{abstract}
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\noindent
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Citizen science has become indispensable for biodiversity monitoring,
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generating billions of observations annually through platforms like eBird
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and iNaturalist. However, citizen science data suffers from systematic
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biases---spatial clustering near population centers, temporal clustering
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on weekends and holidays, and taxonomic bias toward charismatic
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species---that limit its scientific utility. We present \textbf{ZooPlatform},
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a gamified citizen science platform that combines AI-assisted species
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identification, adaptive sampling incentives, multi-source data quality
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estimation, and blockchain-verified contribution records to produce
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research-grade biodiversity data at scale. ZooPlatform's gamification
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engine uses reinforcement learning to personalize challenges that
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direct observer effort toward data-scarce regions, times, and taxa,
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reducing spatial sampling bias by 47\% and taxonomic bias by 34\%
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compared to unguided observation. A Bayesian data quality model
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integrates observer expertise, AI confidence, and environmental context
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to assign per-observation reliability scores, enabling downstream
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analyses to appropriately weight heterogeneous data. ZooPlatform has
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enrolled 23{,}847 contributors across 41 countries over 14 months,
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generating 4.2 million verified observations covering 12{,}394 species.
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Contributors earn ZooRep tokens for verified contributions, which
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integrate with Zoo DAO governance (ZOO-GOV-2026). All data is released
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under CC-BY-4.0 through the Zoo Open Science Network.
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\medskip
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\noindent\textbf{Keywords:} citizen science, gamification, data quality,
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AI-assisted identification, sampling bias, biodiversity monitoring,
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contribution incentives
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\end{abstract}
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\newpage
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\tableofcontents
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\newpage
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% ══════════════════════════════════════════════════════════════════════════════
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\input{sections/01-introduction}
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\input{sections/02-related-work}
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\input{sections/03-platform-architecture}
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\input{sections/04-adaptive-gamification-engine}
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\input{sections/05-ai-assisted-identification}
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\input{sections/06-bayesian-data-quality-model}
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\input{sections/07-token-incentive-system}
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\input{sections/08-evaluation}
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\input{sections/09-discussion}
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\input{sections/10-implementation}
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\input{sections/11-future-work}
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\input{sections/99-conclusion}
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\end{document}
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