🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 31 (from laksa071)

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/2227-9091/12/7/104\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2227-9091/12/7/104\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.mdpi.com\/2227-9091\/12\/7\/104","crawl_time":1776446781,"first_indexed_time":1719391758,"http_code":200,"src_unparsed":"com,mdpi!www,\/2227-9091\/12\/7\/104 s443","src_root_hash":"11974975279136771031","history_drop_reason":null,"meta_title":"Inference for the Parameters of a Zero-Inflated Poisson Predictive Model","meta_descriptions":["In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed."],"meta_canonical":null,"ml_categories_json":"{\"\/Science\":754,\"\/Finance\":656,\"\/Finance\/Insurance\":651,\"\/Finance\/Insurance\/Other\":419,\"\/Science\/Computer_Science\":283,\"\/Science\/Computer_Science\/Machine_Learning_and_Artificial_Intelligence\":269}","ml_types_json":"{\"\/Article\":995,\"\/Article\/Study_or_Research_Findings\":976}","ml_intent_types_json":"{\"Informational\":999}","meta_language":"en","attrs_author":null,"attrs_publish_time":0,"attrs_original_publish_time":1719391758,"attrs_is_republished":0,"attrs_nr_words":"16847","attrs_boilerpipe_nr_words":"10020","body_ext_links_number":91,"body_int_links_number":81,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":1,"src_redirect":"","download_time_msec":629,"download_ttfb_msec":615,"download_size":86211}

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`	1.6 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/2227-9091/12/7/104

Last Crawled

2026-04-17 17:26:21 (1 month ago)

First Indexed

2024-06-26 08:49:18 (1 year ago)

HTTP Status Code

200

Content

Meta Title

Inference for the Parameters of a Zero-Inflated Poisson Predictive Model

Meta Description

In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.

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		75.4%
/Finance		65.6%
/Finance/Insurance		65.1%
/Finance/Insurance/Other		41.9%
/Science/Computer_Science		28.3%
/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence		26.9%

Raw JSON

{
    "/Science": 754,
    "/Finance": 656,
    "/Finance/Insurance": 651,
    "/Finance/Insurance/Other": 419,
    "/Science/Computer_Science": 283,
    "/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence": 269
}

ML Page Types

/Article		99.5%
/Article/Study_or_Research_Findings		97.6%

Raw JSON

{
    "/Article": 995,
    "/Article/Study_or_Research_Findings": 976
}

ML Intent Types

Informational

99.9%

Raw JSON

{
    "Informational": 999
}

Content Metadata

Language

Author

null

Publish Time

not set

Original Publish Time

2024-06-26 08:49:18 (1 year ago)

Republished

Word Count (Total)

16,847

Word Count (Content)

10,020

Links

External Links

Internal Links

Technical SEO

Meta Nofollow

Meta Noarchive

JS Rendered

Yes

Redirect Target

null

Performance

Download Time (ms)

629

TTFB (ms)

615

Download Size (bytes)

86,211

Location

Host ID

31 (laksa031)

Partition ID

Root Hash

11974975279136771031

Unparsed URL

com,mdpi!www,/2227-9091/12/7/104 s443