Bibliographic record
Abstract
Attacking currency trends charts, analyzing, 236-249 charts, selecting, 234, 236 currency pair, selecting, 234-236 direction of trend, defining, 234, 235 nontrending clues and transition to trending market, 249-252 overview, 275, 276 patience in, 234, 260, 275 preparing for trade, 233, 234 rules for.See Rules for attacking the trend tools for.See Technical tools Attributes of successful currency traders fear, controlling, 61-63 greediness, 63-66 practice, 52-54 risk tolerance, 54-58 skill or aptitude, 49-52 trading plan, 58-61 Australian dollar (AUD), 23, 32, 33, 43, 44, 126Bandwagon trades, 57, 58 Bank of Canada (BOC), 42 Bank of England (BOE), 42, 235 Bank of Japan (BOJ), 42, 43, 235-236 Barclay, Scott, 118 Bearish Highway, 118, 119, 153, 157, 166 Bearish trend lines, 137-139, 141, 180, 183-187, 192, 204 Bernanke, Ben, 39 Bid-to-ask spread, 24, 25, 27, 37, 235 Blind luck, 72-74, 77 Bollinger Band, 90 Borderlines charts and, 76, 77, 236-250, 271-275 described, 57, 76-78, 95 Fibonacci retracements and, 142, 144, 145, 209-211, 215-218, 230, 231, 255.See also Fibonacci retracements managing the trade, 256-264 moving averages, 97, 133, 135, 144, 155, 157-161, 168, 169, 255.See also Moving average (MA) risk and, 56-58, 76, 79, 82, 83, 95, 266, 267 trend lines, 140, 144, 181, 186-189, 193, 197-204, 255.See also Trend lines trend moves and, 255 use of, 236, 269-275 British pound (GBP), 23, 33, 38, 44, 126, 174, 235 Bullish Highway, 118-120, 153, 157, 163, 170, 267, 274 Bullish trend lines, 113, 137-139, 180
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.797 | 0.751 |
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".