A bio-optical model for the estimation of chlorophyll a using animal-borne instruments in an optically complex ecosystem
Bibliographic record
Abstract
Studies using marine animals instrumented with biologging devices to estimate phytoplankton biomass have typically omitted continental shelf regions due to the confounding effects of optically active constituents other than phytoplankton present. The lack of algorithms for these regions is problematic, as they are some of the most biologically productive in the world and are often inhabited by the species of interest. We developed a bio-optical model to estimate chlorophyll a concentration (chl a) using light attenuation (LA) measured using a standard oceanographic instrument in an optically complex water body that is applicable to data collected by animal-borne devices. To achieve this, we conducted a replicated experiment to compare measurements made using time-depth-light recorders (TDLRs) to those of a standard oceanographic instrument (the HyperPro) in an adjacent water body, the Bedford Basin, Nova Scotia, Canada. Measurements of LA made by TDLRs were comparable to those of the HyperPro atdepth. The best supported bio-optical model for the estimation of chl a included both LA measured by the HyperPro and season as a fixed effect. The use of animal-borne devices to collect subsurface chl a data not only provides an opportunity to collect valuable oceanographic data but also allows for the exploration of broader ecological questions relating to the influence of primary productivity on the movement patterns of wide-ranging marine species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".