Analyzes text for machine-generated content signals using five weighted heuristics: sentence length burstiness, vocabulary richness, filler phrase density, passive voice density, and paragraph length uniformity. Returns a 0-100 probability score with a classification label. Fully self-contained with no third-party dependencies.
{"success":true,"data":{"ai_probability":43,"label":"uncertain","word_count":22,"sentence_count":1,"plan":"free"}}
curl --location --request POST 'https://zylalabs.com/api/12675/sitetrace+content+detector+api/24553/analyze+text+for+machine+content?text=Furthermore, in order to delve into the comprehensive framework of this topic, it is important to leverage synergies across all relevant touchpoints.' --header 'Authorization: Bearer YOUR_API_KEY'
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Authorization
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[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
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The endpoint returns a JSON object containing a success status, a probability score (0-100) indicating the likelihood of machine-generated content, a classification label, and a breakdown of text signals such as word and sentence counts.
Key fields in the response include `ai_probability` (the likelihood score), `label` (classification of the text), `word_count` (total words), `sentence_count` (total sentences), and `success` (indicates if the request was processed successfully).
The response data is structured as a JSON object. It contains a `success` boolean field and a `data` object that holds the detailed analysis results, including the probability score and classification label.
Typical use cases include academic integrity checks, content moderation, and automated content analysis to identify potential machine-generated text in articles, essays, or online posts.
Users can customize their requests by submitting different text inputs to the Analyze Text endpoint. The API analyzes the provided text based on its heuristics without requiring additional parameters.
Data accuracy is maintained through the use of five weighted heuristics that analyze various text characteristics. This statistical approach helps ensure reliable detection of machine-generated content.
Standard data patterns include a range of `ai_probability` scores from 0 to 100, with corresponding labels such as "certain," "likely," "uncertain," or "unlikely," indicating the confidence level of the analysis.
In the response, `ai_probability` indicates the likelihood of machine generation, `label` categorizes the text's classification, `word_count` shows the total number of words analyzed, and `sentence_count` reflects the number of sentences in the input text.
O endpoint pode analisar qualquer texto escrito, incluindo artigos, ensaios, postagens em blogs e comentários. Ele é projetado para detectar sinais de conteúdo gerado por máquina, independentemente do tema ou estilo do texto.
A pontuação de `ai_probability` varia de 0 a 100, onde valores próximos a 100 indicam alta probabilidade de conteúdo gerado por máquina, enquanto valores próximos a 0 sugerem conteúdo humano. A interpretação deve considerar o rótulo associado para contexto adicional.
O rótulo de classificação fornece uma avaliação qualitativa do texto analisado, categorizando-o como "certo," "provável," "incerto," ou "improvável" em relação à geração por máquina. Isso ajuda os usuários a entenderem a confiança da análise.
A API pode retornar uma pontuação de probabilidade mesmo para textos curtos, mas a precisão pode ser reduzida. Para textos vazios, a API pode retornar um erro ou uma pontuação padrão, indicando que não há dados suficientes para análise.
A API utiliza cinco heurísticas ponderadas: explosão do comprimento das frases, riqueza do vocabulário, densidade de frases de enchimento, densidade de voz passiva e uniformidade do comprimento dos parágrafos. Essas heurísticas ajudam a identificar padrões de texto gerado por máquina.
Os usuários podem usar a pontuação de `ai_probability` e o rótulo de classificação para filtrar ou priorizar textos para revisão. Além disso, as contagens de palavras e frases podem ser úteis para análises estatísticas ou relatórios.
O endpoint aceita texto em formato simples, sem restrições específicas sobre formatação. Os usuários devem garantir que o texto esteja em um formato legível, como texto plano, para que a análise seja eficaz.
A qualidade dos resultados é garantida por meio da aplicação rigorosa das cinco heurísticas, que foram desenvolvidas com base em análises estatísticas. Isso permite uma detecção confiável de conteúdo gerado por máquina, minimizando falsos positivos.
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