Bibliographic record
Abstract
In 2006, a tumult arose in the world of fisheries. A controversial paper titled “Impacts of biodiversity loss on ocean ecosystem services” by Worm et al. (2006) was published in Science. The paper was sensational since it alluded to a prediction that global populations of marine fish (finfish and invertebrates) will be 100% collapsed by 2048. The paper was written by a group of marine ecologists and economists in which Boris Worm from Dalhousie University Canada led the authorship. After the paper was published, the issue of fish disappearance in 2048 became hot topics in the world’s mass media. In fact, the Worm et al. paper triggered the debates among researchers. Over time the debates heated up. Surprisingly, a reconciliation took place in 2009, marked by a collaboration between Worm’s team and his critics, writing another paper in Science. The present essay reaffirms the invalidity of the global collapse prediction in 2048 as revealed by many researchers. It is also shown that the Worm et al. paper did not state that all fish will disappear and through the joint paper in 2009, Worm and colleagues have indirectly rectified the prediction already.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".