The fitness landscape of a community of Darwin’s finches
Bibliographic record
Abstract
Purpose The dataset and script were developed to estimate the fitness landscape for Darwin's ground finch species (Geospiza spp.) at El Garrapatero over 2003 to 2020, and use the fitness landscape to consider theoretical expectations and previous empirical assertions regarding the topology of fitness and adaptive landscapes. Brief Methodology To fulfil these aims, we used the data from our long-term monitoring site El Garrapatero on Santa Cruz in the Galápagos, Ecuador. We calculated lifespan as a fitness proxi from our recapture data to construct a fitness and adaptive landscape using the beak length and depth. Data Please, download and consult the README text file which explains the contents of adaptive.landscapes.finches.zip. The .zip file preserves the folder structure needed to run the scripts. The main program needed for the analysis is R (open-source), but to fully reproduce all the code, ImageMagick (open-source) and FFMPEG (open-source) programs. References GitHub repository of 'adaptive.landscapes.finches' The scripts and data and for the R language (R Core Team 2023; R version, 4.2.1 (Funny-Looking Kid)).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".