MEL: Benthic samples collected by divers with a suction dredge from Bedeque Bay, an estuary in Prince Edward Island, Canada in 1967.
Bibliographic record
Abstract
Bedeque Bay, an estuary in Prince Edward Island, yields a consistently large crop of oysters. In 1967, oyster production throughout Prince Edward Island in general was declining and a benthic survey of the main fishing area in the bay was conducted in order to identify the ecological factors or gradients causing the observed spatial composition of the benthos. The benthic survey was carried out between July 12th and August 14th, 1967, just following the oyster fishing season. Samples were collected underwater by SCUBA divers using a modified suction dredge. The area enclosed by a 1 m2 quadrat was sampled with the dredge (which was equipped with a 4 mm mesh collecting bag) to a depth of 50 era. Forty four stations, covering most of the estuary, were each sampled by a single quadrat. Animals were counted and plants recorded by dry weight. Only subjective classifications of sediment type based on field observations were made. These measurements are not part of this version of the dataset. Species distribution information associated with this study were digitized, standardized and republished as part of the Atlantic Coastal Zone Information Steering Committee (ACZISC) Atlantic Ecosystem Initiative (AEI) funded project “Atlantic Canada’s Biological Data for Ecosystem Planning and Decision-making” and OBIS Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".