High‐resolution characterization of the abiotic environment and disturbance regimes on the Great Barrier Reef, 1985–2017
Bibliographic record
Abstract
This data compilation synthesizes 36 static environmental and spatial variables, and temporally explicit modeled estimates of three major disturbances to coral cover on the Great Barrier Reef (GBR): (1) coral bleaching, (2) tropical cyclones, and (3) outbreaks of the coral-eating crown-of-thorns starfish Acanthaster cf. solaris. Data are provided on a standardized grid (0.01° × 0.01° ~ 1 × 1 km) for reef locations along the GBR, containing 15,928 pixels and excluding the northernmost sections (<12° S) where empirical data were sparse. This compilation provides a consistent and high-resolution characterization of the abiotic environment and disturbance regimes for GBR reef locations at a fine spatial scale to be used in the development of complex ecosystem models. Static estimates of environmental variables (e.g., depth, bed shear stress, average temperature, temperature variation) originally developed by the Commonwealth of Australia's Environment Research Facility (CERF) Marine Biodiversity Hub were provided by Geoscience Australia. Annual (1985-2017) disturbance estimates were either interpolated from empirical data (A. cf. solaris), predicted from proxy indicators (e.g., degree heating weeks [DHW] as a proxy for bleaching severity), or explicitly modeled (e.g., wave height model for each cyclone). This data set synthesizes some of the most recent advances in remote sensing and modeling of environmental conditions on the GBR; yet it is not exhaustive and we highlight areas that should be expanded through future research. The characterization of abiotic and disturbance regimes presented here represent an essential tool for the development of complex regional scale models of the GBR; preventing redundancy between working groups and promoting collaboration, innovation, and consistency. When using the data set, we kindly request that you cite this article and/or the articles cited in the reference section, recognizing the work that went into compiling the data together and the original authors' willingness to make it publicly available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".