🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 31 (from laksa150)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa031.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, meta_canonical AS meta_canonical, ml_categories_json AS ml_categories_json, ml_types_json AS ml_types_json, ml_intent_types_json AS ml_intent_types_json, meta_language AS meta_language, attrs_author AS attrs_author, ifNull(toUnixTimestamp(attrs_publish_time), 0) AS attrs_publish_time, ifNull(toUnixTimestamp(attrs_original_publish_time), 0) AS attrs_original_publish_time, ifNull(attrs_is_republished, 0) AS attrs_is_republished, ifNull(attrs_nr_words, 0) AS attrs_nr_words, ifNull(attrs_boilerpipe_nr_words, 0) AS attrs_boilerpipe_nr_words, ifNull(body_ext_links_number, 0) AS body_ext_links_number, ifNull(body_int_links_number, 0) AS body_int_links_number, ifNull(meta_nofollow, 0) AS meta_nofollow, ifNull(meta_noarchive, 0) AS meta_noarchive, ifNull(props_was_rendered, 0) AS props_was_rendered, ifNull(src_redirect, \'\') AS src_redirect, ifNull(download_time_msec, 0) AS download_time_msec, ifNull(download_ttfb_msec, 0) AS download_ttfb_msec, ifNull(download_size, 0) AS download_size FROM crawler3.page_info_local FINAL PREWHERE int_partition_id = 55 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2079-3197/11/3/52\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2079-3197/11/3/52\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.mdpi.com\/2079-3197\/11\/3\/52","crawl_time":1776230498,"first_indexed_time":1678112005,"http_code":200,"src_unparsed":"com,mdpi!www,\/2079-3197\/11\/3\/52 s443","src_root_hash":"11974975279136771031","history_drop_reason":null,"meta_title":"Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions","meta_descriptions":["Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN’s building blocks, their roles, and other vital issues."],"meta_canonical":null,"ml_categories_json":"{\"\/Science\":587,\"\/Science\/Computer_Science\":582,\"\/Science\/Computer_Science\/Machine_Learning_and_Artificial_Intelligence\":576,\"\/Computers_and_Electronics\":567,\"\/Computers_and_Electronics\/Software\":553,\"\/Computers_and_Electronics\/Software\/Educational_Software\":330}","ml_types_json":"{\"\/Article\":992,\"\/Article\/Study_or_Research_Findings\":702}","ml_intent_types_json":"{\"Informational\":999}","meta_language":"en","attrs_author":null,"attrs_publish_time":0,"attrs_original_publish_time":1678112005,"attrs_is_republished":0,"attrs_nr_words":"14371","attrs_boilerpipe_nr_words":"10867","body_ext_links_number":192,"body_int_links_number":78,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":1,"src_redirect":"","download_time_msec":800,"download_ttfb_msec":783,"download_size":85747}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📍

LOCATION

Host 31 · Partition 55

laksa031

11974975279136771031

📄

INDEXABLE

✅

CRAWLED

1 month 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`	1.7 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	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property

Value

URL

https://www.mdpi.com/2079-3197/11/3/52

Last Crawled

2026-04-15 05:21:38 (1 month ago)

First Indexed

2023-03-06 14:13:25 (3 years ago)

HTTP Status Code

200

Content

Meta Title

Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions

Meta Description

Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN’s building blocks, their roles, and other vital issues.

Meta Canonical

null

Boilerpipe Text

heavy column, fetched on demand

Markdown

heavy column, fetched on demand

Readable Markdown

heavy column, fetched on demand

ML Classification

ML Categories

/Science		58.7%
/Science/Computer_Science		58.2%
/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence		57.6%
/Computers_and_Electronics		56.7%
/Computers_and_Electronics/Software		55.3%
/Computers_and_Electronics/Software/Educational_Software		33.0%

Raw JSON

{
    "/Science": 587,
    "/Science/Computer_Science": 582,
    "/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence": 576,
    "/Computers_and_Electronics": 567,
    "/Computers_and_Electronics/Software": 553,
    "/Computers_and_Electronics/Software/Educational_Software": 330
}

ML Page Types

/Article		99.2%
/Article/Study_or_Research_Findings		70.2%

Raw JSON

{
    "/Article": 992,
    "/Article/Study_or_Research_Findings": 702
}

ML Intent Types

Informational

99.9%

Raw JSON

{
    "Informational": 999
}

Content Metadata

Language

Author

null

Publish Time

not set

Original Publish Time

2023-03-06 14:13:25 (3 years ago)

Republished

Word Count (Total)

14,371

Word Count (Content)

10,867

Links

External Links

192

Internal Links

Technical SEO

Meta Nofollow

Meta Noarchive

JS Rendered

Yes

Redirect Target

null

Performance

Download Time (ms)

800

TTFB (ms)

783

Download Size (bytes)

85,747

Location

Host ID

31 (laksa031)

Partition ID

Root Hash

11974975279136771031

Unparsed URL

com,mdpi!www,/2079-3197/11/3/52 s443