Bibliographic record
Abstract
As the number of Internet sites grew exponentially along with the growth of the World Wide Web, finding specific locations became accordingly more challenging. URLs, or uniform resource locators, were developed to serve as Internet addresses and facilitate location of information. A URL contains a descriptor, or series of descriptors of a given location, followed by a suffix. The suffix, or domain, is set apart by a period followed by a standardized abbreviation that denotes its nature. The domain is that part of an Internet address that denotes the nature of the entity that posted the site. The most famous and largest domain is the ubiquitous “.com”. The domain name suffix, known as the top-level domain or TLD, serves as a vital indicator in a URL. The number of TLDs is limited, with their adoptions determined by the Internet Corporation for Assigned Names and Numbers, or ICANN. Prior to 2000, the accepted top-level domains were the following: .gov-Government agencies .edu-Educational institutions .org-Organizations (nonprofit) .mil-Military .com-Commercial business .net-Network organizations Countries could also have domains such as the following: .ca-Canada .au-Australia .uk-Great Britain As of 2000, Icann approved seven new top-level domains to augment the original list. These TLDs included the following: .biz-Business .info-Unrestricted (open to any use) .name-Individuals .pro-Accountants, lawyers, physicians, and other professions .museum-Museums .aero-Air transport industry .coop-cooperatives While “dot-com” has become a cliché, it is clear that the designators now called TLDs are influential in bringing order to the myriad number of Internet sites on the World Wide Web. Whether the current presentation of TLDs is sufficient or more may need to be designated in the future remains to be seen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.189 | 0.224 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".