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Datasets and calibration

GET /v1/datasets lists every dataset Bewary answers from, with its geography (worldwide or a country code), what it holds and its live counts. It is free and needs no key. Every signal in an answer names the dataset it came from, so a buyer always knows what a verdict rests on. The site score is calibrated on labelled hosts, and its table is published at GET /v1/calibration/site.

Every dataset, with its geography

GET /v1/datasets is free and needs no key. It lists every dataset Bewary answers from:

FieldMeaning
id, nameThe dataset, such as phone_complaints (US phone complaints) or phone_reports_de (German regulator number actions)
coverageWW for worldwide, or the country code of a national list, such as US or DE
kindscam_list, reference, own_traffic or model
entity_typesWhat it holds: domains, links, phone numbers, wallets, IBANs and so on
active_entriesEntries in force now, for a scam list
target_countries, entity_countriesEntries by the country they are aimed at, and by the country they are from
refreshed_atThe last time it was read

The data page shows the same list by country, read live from the API.

Where each answer came from

Every signal in an answer names its dataset and that dataset’s coverage, and every entity lists all the datasets checked for it, with in_coverage: false when a dataset does not cover the value, such as a US-only list for a German number. So “not on any list” is always “not on these lists, which cover this value”.

The calibration behind the site score

A domain whose page Bewary’s worker read in the last 90 days carries site.p_scam_site: the share of scam sites among labelled hosts with the same score, calibrated monotone and published at GET /v1/calibration/site, free. The table gives each score band, how many labelled hosts fell in it, how many were scam sites, the probability served and its 95% interval. It is refit as the nightly audit labels more hosts. The envelope’s own p_scam is not calibrated yet: models.calibration says interim-band-v0.

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