Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hi-tech fraudsters have urbanized a new way of tricking on line banking customers. One such most well known and fast growing technique is phishing. Latest in phishing is application of Trojan program. Trojan horse program insinuates itself into a user's computer via an email and directs the user of the system to website which is exactly similar to financial institution web site. Crooks pick up passwords and account numbers as soon as customer logon to these sites. As it evident from table 1 phishing causes maximum loss to the customers/ institution in comparison to other similar techniques. Keeping in view, the serious threats of phishing attacks author analyzed the trends of major activities of the phishing across globe specifically in the banking sector. In addition, author analyzed the reasons for increase in fishng activities, types of phishing techniques, and process of phishing. Further author has presented recent cases of phishing specifically in banking/ financial sector. Towards the end it author has studied the measures to combat the fishing in online banking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it