Degradation of Caribbean coral reefs: focusing on proximal rather than ultimate drivers. Reply to Rogers
Bibliographic record
Abstract
In recent decades, coral reefs worldwide have been declining at the same time that ocean temperature, fishing and coastal development (and associated pollution and loss of habitat) have increased. Unfortunately, the causality and strength of possible relationships remains a matter of debate (e.g. Aronson et al. 2004; Grigg et al. 2005; Mora et al. 2007). Using region-wide biological, environmental and anthropogenic databases, in combination with statistical methods to control for spatial autocorrelation and collinearity among predictors, I (Mora 2008) demonstrated that humans, through mechanisms associated with agricultural land use, coastal development, fishing and increases in ocean temperature have been responsible for various overwhelming conditions in coral reefs throughout the Caribbean. Rogers (2009) claims that this region-scale analysis underestimated the roles of bleaching and infectious diseases, therefore creating an incorrect and misleading perspective. It should be noted that my paper (Mora 2008) was intended to address the root causes of coral reef degradation (see §1 in Mora 2008). By narrowing the scope of the problem of coral reef degradation, Rogers (2009) failed to realize that bleaching and outbreaks of infectious diseases are proximal drivers caused by upper-level or ultimate stressors, which were the main aim of my paper (Mora 2008). Although all drivers are worth scientific interest, from the ecological and conservation point of view, it is the identification and resolution of ultimate drivers that should be a priority for the conservation of coral reefs.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.030 | 0.051 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".