Bibliographic record
Abstract
This chapter describes how the location of Fibonacci retracement levels can become a deciding factor in making decisions from both a trading and investing standpoint. Fibonacci analysis can be applied over any time frame and is equally effective in mapping short-term intraday swings and daily gyrations. However, the real value-added offered by Fibonacci retracements (Fibo) can be specifically gleaned from their application to long-term secular charts. The Fibonacci retracements are applied to the bull move from the 1970s into the 1980 secular peak, marked by a dashed line and stretching to the left edge of the chart. The overlaid Fibonacci retracements pertain to the deep downside move from that peak to the March 1980 spike low below $500. Often the Fibo retracement levels will coincide with prices that previously defined congestion phases. You can see this when noting the current situation in gold, where the 38 percent Fibo retracement level lines up with the sideways congestion period in the first quarter of 2006.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".