Ground-truthing the ground-truth: reply to Garibaldi et al.'s comment on “Managing fisheries from space: Google Earth improves estimates of distant fish catches”
Bibliographic record
Abstract
There has been a growing interest in the potential of Google Earth for scientific inquiries, and our previous paper (Al-Abdulrazzak and Pauly, 2014. Managing fisheries from space: Google Earth improves estimates of distant fish catches. ICES Journal of Marine Science, 71: 450–454) on weirs and their catch in the Persian Gulf is a case in point. Garibaldi et al. (2014. Comment on: “Managing fisheries from space: Google Earth improves estimates of distant fish catchs” by Al-Abdulrazzak and Pauly. ICES Journal of Marine Science), while agreeing in principle with using Google Earth for fisheries-related purposes, criticized the assumptions, data, methodology, and results of this paper. Here, we refute their criticisms, notably by showing that the “derelict weirs” that they thought they had “ground-truthed” are not weirs at all, but another type of fishing gear in one case, and debris from a boat anchoring system in the other. We develop the theme that ground-truthing requires local knowledge, and provide recommendations for using Google Earth images in fisheries management.
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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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.060 | 0.110 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".