Series of box plots showing the difference in autocorrelation measured using Moran's along lines parallel to the two intercardinal axes.
Bibliographic record
Abstract
<p>Figure A displays the results for landscapes generated by employing both synthesis algorithms with a Hurst exponent of (high heterogeneity). Figure B displays similar results for landscapes generated with a Hurst exponent of (intermediate heterogeneity) whilst figure C shows results for landscapes generated using a Hurst exponent of (low heterogeneity). The dashed red line shows the location of no difference between the autocorrelation measured in each axis direction (where Moran's is zero) and represents the expected median for a series of isotropic landscapes. Lighter coloured boxes show the inter-quartile range of the results from a synthesis algorithm with a median of Moran's closer to zero at the respective distance class than the alternative algorithm. Conversely, darker coloured boxes indicate that the magnitude of the median value of for a given synthesis algorithm exceeds that exhibited by landscapes generated using the alternative. The notches of the box plots extend from the median to multiplied by the inter-quartile range divided by the square root of the sample size (in this case 100) representing a rough 95% confidence interval for the median based on asymptotic normality <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017040#pone.0017040-McGill1" target="_blank">[35]</a>. The box whiskers extend to the full range of data values.</p>
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".