occlupanid data writes that the Holotypic Occlupanid Research Group hosts several years of research classifying occlupanids, small ubiquitous objects dotting supermarket aisles and sidewalks, as the most common yet puzzling member of phylum Plasticae within a synthetic taxonomy database.
- The site catalogs dozens of families such as Acutignathidae, Archignathidae, Corrugatidae and Toxodentidae with individual species pages.
- Navigation includes Identification Guide, Publications and Reports, Cartonalia: The Occlupanopsida, and a Guide to symbols for ecological, geographical and taxonomic classification.
- The project also covers morphology, growth and development, origins of the Occlupanida, history of occlupanology, and a Pseudo-occlupanids section.
- The site catalogs dozens of families such as Acutignathidae, Archignathidae, Corrugatidae and Toxodentidae with individual species pages.
- Navigation includes Identification Guide, Publications and Reports, Cartonalia: The Occlupanopsida, and a Guide to symbols for ecological, geographical and taxonomic classification.
- The project also covers morphology, growth and development, origins of the Occlupanida, history of occlupanology, and a Pseudo-occlupanids section.
Firecrawl introduces pdf-inspector, a high-performance Rust library designed for rapid PDF classification, text extraction, and Markdown conversion. By sampling content streams to quickly distinguish between text-based and scanned documents, the tool enables intelligent routing that bypasses costly OCR services for standard PDFs. It delivers position-aware text extraction, automated table and column detection, and robust encoding handling while maintaining a lightweight footprint with no external ML dependencies or model training requirements.
- Provides bindings for Python, Node.js, and browser WebAssembly environments.
- Achieves sub-200ms processing times on large corpora while outperforming several established local parsers in reading order and table accuracy.
- Features per-page OCR routing suggestions to optimize mixed-format document workflows.
- Handles complex layouts including RTL text, multi-column newspapers, and CID-encoded fonts.
- Released under the MIT license with active community contributions and CI/CD automation.
- Provides bindings for Python, Node.js, and browser WebAssembly environments.
- Achieves sub-200ms processing times on large corpora while outperforming several established local parsers in reading order and table accuracy.
- Features per-page OCR routing suggestions to optimize mixed-format document workflows.
- Handles complex layouts including RTL text, multi-column newspapers, and CID-encoded fonts.
- Released under the MIT license with active community contributions and CI/CD automation.
This tutorial demonstrates how to implement an intelligent routing layer using NadirClaw to optimize Large Language Model (LLM) costs. The system classifies prompts into simple or complex tiers locally before selecting the most appropriate model, such as switching between Gemini Flash and Pro versions. It covers installation, local classification testing via CLI, visualizing decision boundaries through centroid-based similarity scores, running a proxy server for live routing, and calculating estimated cost savings compared to using high-end models exclusively.
This article introduces Scikit-LLM, a Python library that integrates large language models like OpenAI's GPT with the Scikit-learn framework to simplify text analysis tasks. It explains and demonstrates two primary classification methods: zero-shot classification, which assigns labels based solely on the model's general knowledge without prior examples, and few-shot classification, which uses a small set of labeled examples within the prompt to improve accuracy. By following a Scikit-learn-style workflow using fit() and predict() methods, users can easily implement these advanced NLP techniques for tasks such as sentiment analysis and topic labeling.
A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required.
Researchers have categorized these states based on three main approaches: the nature of the experience itself (state-based), the method of induction (method-based), and underlying neurophysiological mechanisms (neuro/physio-based). Current research focuses on identifying overlapping phenomenological features across different ASCs, aiming to improve nuanced conceptualization and measurement, particularly for potential clinical applications like psychedelic-assisted psychotherapy.
- Altered states of consciousness (ASC) have been classified along different criteria.
- State-based schemes use features of subjective experience for the classification.
- Method-based schemes distinguish how or by which means an ASC is induced.
- Neuro/Physio-based schemes detail biological mechanisms.
- Across state-based schemes we extracted terms that suggest key subjective features of ASCs. A clustering analysis revealed eight core features of ASCs.
- Altered states of consciousness (ASC) have been classified along different criteria.
- State-based schemes use features of subjective experience for the classification.
- Method-based schemes distinguish how or by which means an ASC is induced.
- Neuro/Physio-based schemes detail biological mechanisms.
- Across state-based schemes we extracted terms that suggest key subjective features of ASCs. A clustering analysis revealed eight core features of ASCs.
This notebook provides an introduction to Naive Bayes classification, covering concepts, formulas, and implementation.
This article discusses how to apply vision language models (VLMs) to document understanding, covering application areas like agentic use cases, question answering, classification, and information extraction, as well as limitations like cost and processing long documents.
A deep dive into advanced evaluation for data scientists, discussing why accuracy is often misleading and exploring alternative metrics for classification and regression tasks like ROC-AUC, Log Loss, R², RMSLE, and Quantile Loss.
The article discusses using Large Language Model (LLM) embeddings as features in traditional machine learning models built with scikit-learn. It covers the process of generating embeddings from text data using models like Sentence Transformers, and how these embeddings can be combined with existing features to improve model performance. It details practical steps including loading data, creating embeddings, and integrating them into a scikit-learn pipeline for tasks like classification.
