🕷️ Crawler Inspector

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Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 57 (from laksa118)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa057.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://chatpaper.com/chatpaper/paper/186913\')), getAhrefsUnparsedNoserviceFromURL(\'https://chatpaper.com/chatpaper/paper/186913\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/chatpaper.com\/chatpaper\/paper\/186913","crawl_time":1766561259,"first_indexed_time":0,"http_code":200,"src_unparsed":"com,chatpaper!\/chatpaper\/paper\/186913 s443","src_root_hash":"6477969075254838257","history_drop_reason":null,"meta_title":"AI-Based Applied Innovation for Fracture Detection in X-rays Using Custom CNN and Transfer Learning Models","meta_descriptions":[],"attrs_boilerpipe_text":"Amna Hassan;\nIlsa Afzaal;\nNouman Muneeb;\nAneeqa Batool;\nHamail Noor\nBone fractures present a major global health challenge, often resulting in pain, reduced mobility, and productivity loss, particularly in low-resource settings where access to expert radiology services is limited. Conventional imaging methods suffer from high costs, radiation exposure, and dependency on specialized interpretation. To address this, we developed an AI-based solution for automated fracture detection from X-ray images using a custom Convolutional Neural Network (CNN) and benchmarked it against transfer learning models including EfficientNetB0, MobileNetV2, and ResNet50. Training was conducted on the publicly available FracAtlas dataset, comprising 4,083 anonymized musculoskeletal radiographs. The custom CNN achieved 95.96% accuracy, 0.94 precision, 0.88 recall, and an F1-score of 0.91 on the FracAtlas dataset. Although transfer learning models (EfficientNetB0, MobileNetV2, ResNet50) performed poorly in this specific setup, these results should be interpreted in light of class imbalance and data set limitations. This work highlights the promise of lightweight CNNs for detecting fractures in X-rays and underscores the importance of fair benchmarking, diverse datasets, and external validation for clinical translation","attrs_markdown":"[![chatpaper](https:\/\/cdn.pdppt.com\/chatpaper\/_nuxt\/logo.CYVlVwPE.png)ChatPaper](https:\/\/chatpaper.com\/chatpaper)\n\n[Sign in]()\n\n- [Interests](https:\/\/chatpaper.com\/chatpaper\/interests)\n- [arXiv](https:\/\/chatpaper.com\/chatpaper)\n- [Venues](https:\/\/chatpaper.com\/chatpaper\/venues)\n- Collection\n\n[Interests](https:\/\/chatpaper.com\/chatpaper\/interests)\n\n[arXiv](https:\/\/chatpaper.com\/chatpaper)\n\n[Venues](https:\/\/chatpaper.com\/chatpaper\/venues)\n\n1\\.[AI-Based Applied Innovation for Fracture Detection in X-rays Using Custom CNN and Transfer Learning Models](https:\/\/arxiv.org\/abs\/2509.06228)\n\n[cs.CV](https:\/\/chatpaper.com\/chatpaper?id=4)09 Sep 2025\n\nAmna Hassan, Ilsa Afzaal, Nouman Muneeb, Aneeqa Batool, Hamail Noor\n\nAmna Hassan; Ilsa Afzaal; Nouman Muneeb; Aneeqa Batool; Hamail Noor\n\nBone fractures present a major global health challenge, often resulting in pain, reduced mobility, and productivity loss, particularly in low-resource settings where access to expert radiology services is limited. Conventional imaging methods suffer from high costs, radiation exposure, and dependency on specialized interpretation. To address this, we developed an AI-based solution for automated fracture detection from X-ray images using a custom Convolutional Neural Network (CNN) and benchmarked it against transfer learning models including EfficientNetB0, MobileNetV2, and ResNet50. Training was conducted on the publicly available FracAtlas dataset, comprising 4,083 anonymized musculoskeletal radiographs. The custom CNN achieved 95.96% accuracy, 0.94 precision, 0.88 recall, and an F1-score of 0.91 on the FracAtlas dataset. Although transfer learning models (EfficientNetB0, MobileNetV2, ResNet50) performed poorly in this specific setup, these results should be interpreted in light of class imbalance and data set limitations. This work highlights the promise of lightweight CNNs for detecting fractures in X-rays and underscores the importance of fair benchmarking, diverse datasets, and external validation for clinical translation\n\nAI Summary\n\nReading and comprehending the paper content, the summary will be generated shortly. Please wait a moment.","attrs_readable_markdown":null,"meta_canonical":"com,chatpaper!\/chatpaper\/paper\/186913 h80"}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

🚫

NOT INDEXABLE

✅

CRAWLED

3 months ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	3.9 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	FAIL	`meta_canonical IS NULL OR = '' OR = src_unparsed`	com,chatpaper!/chatpaper/paper/186913 h80

Page Details

Property	Value
URL	https://chatpaper.com/chatpaper/paper/186913
Last Crawled	2025-12-24 07:27:39 (3 months ago)
First Indexed	not set
HTTP Status Code	200
Meta Title	AI-Based Applied Innovation for Fracture Detection in X-rays Using Custom CNN and Transfer Learning Models
Meta Description	null
Meta Canonical	com,chatpaper!/chatpaper/paper/186913 h80
Boilerpipe Text	Amna Hassan; Ilsa Afzaal; Nouman Muneeb; Aneeqa Batool; Hamail Noor Bone fractures present a major global health challenge, often resulting in pain, reduced mobility, and productivity loss, particularly in low-resource settings where access to expert radiology services is limited. Conventional imaging methods suffer from high costs, radiation exposure, and dependency on specialized interpretation. To address this, we developed an AI-based solution for automated fracture detection from X-ray images using a custom Convolutional Neural Network (CNN) and benchmarked it against transfer learning models including EfficientNetB0, MobileNetV2, and ResNet50. Training was conducted on the publicly available FracAtlas dataset, comprising 4,083 anonymized musculoskeletal radiographs. The custom CNN achieved 95.96% accuracy, 0.94 precision, 0.88 recall, and an F1-score of 0.91 on the FracAtlas dataset. Although transfer learning models (EfficientNetB0, MobileNetV2, ResNet50) performed poorly in this specific setup, these results should be interpreted in light of class imbalance and data set limitations. This work highlights the promise of lightweight CNNs for detecting fractures in X-rays and underscores the importance of fair benchmarking, diverse datasets, and external validation for clinical translation
Markdown	[![chatpaper](https://cdn.pdppt.com/chatpaper/_nuxt/logo.CYVlVwPE.png)ChatPaper](https://chatpaper.com/chatpaper) [Sign in]() - [Interests](https://chatpaper.com/chatpaper/interests) - [arXiv](https://chatpaper.com/chatpaper) - [Venues](https://chatpaper.com/chatpaper/venues) - Collection [Interests](https://chatpaper.com/chatpaper/interests) [arXiv](https://chatpaper.com/chatpaper) [Venues](https://chatpaper.com/chatpaper/venues) 1\.[AI-Based Applied Innovation for Fracture Detection in X-rays Using Custom CNN and Transfer Learning Models](https://arxiv.org/abs/2509.06228) [cs.CV](https://chatpaper.com/chatpaper?id=4)09 Sep 2025 Amna Hassan, Ilsa Afzaal, Nouman Muneeb, Aneeqa Batool, Hamail Noor Amna Hassan; Ilsa Afzaal; Nouman Muneeb; Aneeqa Batool; Hamail Noor Bone fractures present a major global health challenge, often resulting in pain, reduced mobility, and productivity loss, particularly in low-resource settings where access to expert radiology services is limited. Conventional imaging methods suffer from high costs, radiation exposure, and dependency on specialized interpretation. To address this, we developed an AI-based solution for automated fracture detection from X-ray images using a custom Convolutional Neural Network (CNN) and benchmarked it against transfer learning models including EfficientNetB0, MobileNetV2, and ResNet50. Training was conducted on the publicly available FracAtlas dataset, comprising 4,083 anonymized musculoskeletal radiographs. The custom CNN achieved 95.96% accuracy, 0.94 precision, 0.88 recall, and an F1-score of 0.91 on the FracAtlas dataset. Although transfer learning models (EfficientNetB0, MobileNetV2, ResNet50) performed poorly in this specific setup, these results should be interpreted in light of class imbalance and data set limitations. This work highlights the promise of lightweight CNNs for detecting fractures in X-rays and underscores the importance of fair benchmarking, diverse datasets, and external validation for clinical translation AI Summary Reading and comprehending the paper content, the summary will be generated shortly. Please wait a moment.
Readable Markdown	null
Shard	57 (laksa)
Root Hash	6477969075254838257
Unparsed URL	com,chatpaper!/chatpaper/paper/186913 s443