Bibliographic record
Abstract
Abstract Standing crop and production of macroalgae and seagrasses were recorded, together with the main physical and chemical parameters, including nutrient concentrations of the water column and 5-cm top sediment porewater, the grazing pressure and the settled paniculate matter (SPM) in two areas of the central (Lido station) and southern (Petta di Bo station) parts of the Venice lagoon. Then the whole set of data was analysed by multivariate analysis. The highest standing crops of Ulva (Lido station) and Zostera (Petta di Bo station) monitored throughout the year (February 1994–February 1995) were ~ 6.5 and ~ 11.0 kg fwt m −2 , accounting for an annual net production of ~ 20.4 and ~ 20.9 kg fwt m −2 , respectively. The estimated gross production of Zostera was, however, ca 35–55% lower than that of Ulva because of the higher decomposition rate and grazing pressure suffered by the markedly stratified and light-limited free-floating fronds of the macroalga. At the Lido station, the overall grazing pressure accounted for ca 65% of the net Ulva production, but it was found to exceed the total production in the July–August period. Ammonium and orthophosphate concentrations in the water column and sediment porewater were ca 2–3 times higher at the Ulva than at the Zostera station. Considering the N:P atomic ratios, nitrogen, during the quick spring-summer biomass increase, could be temporarily critical for the macrophytic growth, especially at the Lido station. The rates of sediment resuspension and settlement were ca six times higher at the Zostera than at the Ulva station, mainly because of higher sediment coverage by the large free-floating fronds of Ulva . For the contribution of individual variables explained by the principal component analysis, it is shown that the Ulva decomposition at the Lido station and the Zostera production at Petta di Bo were the major factors affecting the total variance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.987 | 0.989 |
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".