Intercalibrating SCOR, NORPAC and bongo nets and the consequences for interpreting decadal‐scale variation in zooplankton biomass in the Gulf of Alaska
Bibliographic record
Abstract
Abstract The Canadian weathership time series of zooplankton wet weight biomass, collected at Ocean Station Papa in the Gulf of Alaska from 1956 to 1981, is one of the most comprehensive of its kind in marine science. In 1966, the sampling gear changed from a white North Pacific (NORPAC) net to a black Scientific Committee on Oceanic Research (SCOR) net and it was recently discovered that insufficient intercalibration samples were collected to understand how their sampling properties differed. A Canada‐GLOBEC project to redo the intercalibration of these net types and to understand how they relate to current sampling gear (bongo net) started in 1997. Seventy replicates of the three net types were collected in deep water in the Gulf of Alaska. The major finding is that all nets have similar sampling characteristics, whereas earlier reports indicated that NORPAC biomass values should be multiplied by a factor of 1.538 to be equivalent to the SCOR net. It now appears that this factor arose because flowmeters were not used in the original 1956–81 sampling (volume filtered was estimated from tow length × mouth area). A positive bias was introduced into the SCOR values because relatively more water passed through the SCOR net (undetected without a flow meter) than through the NORPAC net. This means that the unmetered NORPAC samples from 1956 to 1966 should not be adjusted and the unmetered SCOR values should be reduced by a factor that is related to wire angle. The general effect on the entire series is to lower the average biomass estimates, but more so for the early portion of the series than the later years.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".