🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 177 (from laksa177)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa177.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 = 44 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://repositorio.unal.edu.co/items/cd0983dc-9c94-4e10-b23b-8088e92a3ed7\')), getAhrefsUnparsedNoserviceFromURL(\'https://repositorio.unal.edu.co/items/cd0983dc-9c94-4e10-b23b-8088e92a3ed7\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/repositorio.unal.edu.co\/items\/cd0983dc-9c94-4e10-b23b-8088e92a3ed7","crawl_time":1777556446,"first_indexed_time":1768436855,"http_code":200,"src_unparsed":"co,edu,unal!repositorio,\/items\/cd0983dc-9c94-4e10-b23b-8088e92a3ed7 s443","src_root_hash":"16009544033961288977","history_drop_reason":null,"meta_title":"On the performance of Kernel Density Estimation using Density Matrices","meta_descriptions":["Density estimation methods can be used to solve a variety of statistical and machine learning challenges. They can be used to tackle a variety of problems, including anomaly detection, generative models, semi-supervised learning, compression, and text-to-speech. A popular technique to find density estimates for new samples in a non parametric set up is Kernel Density Estimation, a method which suffers from costly evaluations especially for large data sets and higher dimensions. In this thesis we want to compare the performance of the novel method Kernel Density Estimation using Density Matrices introduced by González et al. [9] against other state-of-the-art fast procedures for estimating the probability density function in different sets of complex synthetic scenarios. Our experimental results show that this novel method is a competitive strategy to calculate density estimates among its competitors and also show advantages when performing on large data sets and high dimensions."],"meta_canonical":null,"ml_categories_json":"{\"\/Science\":830,\"\/Science\/Computer_Science\":764,\"\/Science\/Computer_Science\/Machine_Learning_and_Artificial_Intelligence\":754,\"\/Computers_and_Electronics\":482,\"\/Computers_and_Electronics\/Software\":463,\"\/Computers_and_Electronics\/Software\/Educational_Software\":271}","ml_types_json":"{\"\/Document\":973,\"\/Document\/Research_Paper\":954}","ml_intent_types_json":"{\"Informational\":999}","meta_language":"es","attrs_author":null,"attrs_publish_time":0,"attrs_original_publish_time":1768436855,"attrs_is_republished":0,"attrs_nr_words":"523","attrs_boilerpipe_nr_words":"177","body_ext_links_number":13,"body_int_links_number":65,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":0,"src_redirect":"","download_time_msec":2073,"download_ttfb_msec":1997,"download_size":75671}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📍

LOCATION

Host 177 · Partition 44

laksa177

16009544033961288977

📄

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`	1.1 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://repositorio.unal.edu.co/items/cd0983dc-9c94-4e10-b23b-8088e92a3ed7

Last Crawled

2026-04-30 13:40:46 (1 month ago)

First Indexed

2026-01-15 00:27:35 (4 months ago)

HTTP Status Code

200

Content

Meta Title

On the performance of Kernel Density Estimation using Density Matrices

Meta Description

Density estimation methods can be used to solve a variety of statistical and machine learning challenges. They can be used to tackle a variety of problems, including anomaly detection, generative models, semi-supervised learning, compression, and text-to-speech. A popular technique to find density estimates for new samples in a non parametric set up is Kernel Density Estimation, a method which suffers from costly evaluations especially for large data sets and higher dimensions. In this thesis we want to compare the performance of the novel method Kernel Density Estimation using Density Matrices introduced by González et al. [9] against other state-of-the-art fast procedures for estimating the probability density function in different sets of complex synthetic scenarios. Our experimental results show that this novel method is a competitive strategy to calculate density estimates among its competitors and also show advantages when performing on large data sets and high dimensions.

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		83.0%
/Science/Computer_Science		76.4%
/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence		75.4%
/Computers_and_Electronics		48.2%
/Computers_and_Electronics/Software		46.3%
/Computers_and_Electronics/Software/Educational_Software		27.1%

Raw JSON

{
    "/Science": 830,
    "/Science/Computer_Science": 764,
    "/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence": 754,
    "/Computers_and_Electronics": 482,
    "/Computers_and_Electronics/Software": 463,
    "/Computers_and_Electronics/Software/Educational_Software": 271
}

ML Page Types

/Document		97.3%
/Document/Research_Paper		95.4%

Raw JSON

{
    "/Document": 973,
    "/Document/Research_Paper": 954
}

ML Intent Types

Informational

99.9%

Raw JSON

{
    "Informational": 999
}

Content Metadata

Language

Author

null

Publish Time

not set

Original Publish Time

2026-01-15 00:27:35 (4 months ago)

Republished

Word Count (Total)

523

Word Count (Content)

177

Links

External Links

Internal Links

Technical SEO

Meta Nofollow

Meta Noarchive

JS Rendered

Redirect Target

null

Performance

Download Time (ms)

2,073

TTFB (ms)

1,997

Download Size (bytes)

75,671

Location

Host ID

177 (laksa177)

Partition ID

Root Hash

16009544033961288977

Unparsed URL

co,edu,unal!repositorio,/items/cd0983dc-9c94-4e10-b23b-8088e92a3ed7 s443