Bibliographic record
Abstract
variability on the scale of tens of kilometres, and high-resolution sampling was used to investigate variability of kilometre scale. In the multiscale variability of zooplankton biomass, the maximum variability was observed on the thousands of kilometres scale, with the quantitative values of biomass represented in a form of normalized variance. The local peak of variability of zooplankton biomass in the range of hundreds of kilometres, in the tropical ocean, was probably due to the enhanced water dynamics with the same scale range. This activity is linked with open-ocean mesoscale eddy fields in the tropical zones of both oceans. The other typical feature of the structure of the zooplanktonic fields is its spatial anisotropy, which indicates different properties of spatial variation of parameters along directions through space. The anisotropy was evaluated by means of two-dimensional spatial autocorrelation functions. Two-dimensional correlation ellipses of the zooplankton biomass fields were orientated by their main axes in accordance with the direction of transport of the main water mass, the direction of motion of the eddies, and the orientation of divergence or convergence zones. The spatial heterogeneity of zooplankton biomass distribution in the horizontal plane can be characterized by the frequency of occurrence-of patches. On a log scale the frequency of occurrence of different size patches diminishes proportionally with their linear size. Zooplankton biomass is distributed more heterogeneously than that of phytoplankton (chlorophyll a). Synchronous measurements of the three-level system phytoplankton-mesozooplankton-flying fish (where each component acts as a prey item for the next one) exhibit the same trend of spatial autocorrelation functions, diminishing over the trophic levels on a scale from hundreds to tens of kilometres. This means that relatively heterogeneous fields of predators exist on more uniformly distributed fields of their prey. © 1995 International Council for the Exploration of the Sea
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.991 | 0.994 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".