Diel oxygen production and uptake by Posidonia oceanica meadows at Elba, Italy
Bibliographic record
Abstract
Aquatic eddy covariance oxygen flux was determined over two seagrass (Posidonia oceanica) meadows at Elba, Italy. The first meadow (open-water) was located 300 m from the southwest corner of the island, and was studied over two continuous days from 15 to 18 May 2016. The second meadow (nearshore) was located 60 m from the north shore of the island, and was studied over two discontinuous days on 13 and 25 May 2017. Both meadows were located at 13 m depth. Eddy covaraince instruments were mounted to a lightweight frame and positioned over seagrass meadows such that the measurement volume was approximately 0.3 m above the top of the canopy. Eddy covariance velocity data were collected at 16 Hz with an acoustic Doppler velocimeter (Vector, Nortek-AS, Norway). Measurements of turbulent fluctuations in oxygen concentration were made 2 cm outside the measuring volume of the Vector using an optode minisensor (O2 Minisensor, Pyroscience GmbH, Germany). The 90% response time of the minisensor was less than 0.3 s. Stable oxygen measurements above and within the canopy were determined with galvanic oxygen sensors (OxyGuard, RBR Ltd., Canada). Eddy covariance fluxes were calculated from the product of turbulent fluctuations in vertical water velocity and oxygen concentration according to standard techniques (see Berg et al., 2003 for details on the aquatic eddy covariance technique, doi:10.3354/meps261075). Further details on calculations of flux, and their correction for the nighttime depletion of oxygen within the seagrass canopy, are presented in the linked manuscript (Koopmans et al., 2020, doi:10.3389/fmars.2020.00118).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".