Bibliographic record
Abstract
Text Chemistry is a relationship Guide Ebook by Amy North. The main feature of the program is its 50,000 word eBook that is all about attracting men via text.( Click to Download Text Chemistry Ebook Here) It becomes strategy to get male's attention.When you're in love with a man and would like to get his attention,the product can assist you.It's not a medicine or tools.You won't find any equipment in the package.As its name, it's about text that can make great chemistry between you and man that you love.Well, it's a e-book that will make a lots of ways of lead him to adore you.\n\nAmy North: She's specialist romantic relationship consultant and best-selling writer from Vancouver Canada. She focused on assisting females from around the world to find and keep the man from their dream. Amy north spent many years studying the nature of romantic relationship and this plan is her ultimate result.\n\nText chemistry works by catching mens' attention and makes them addicted to you. All these sms messages have been proven to work on even the most distant and cool man. They'll start longing for you all everyday.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.809 | 0.644 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".