Quantifying spatial and temporal variations in phytoplankton and kelp isotopic signatures to estimate the distribution of kelp‐derived detritus off the west coast of Vancouver Island, Canada
Bibliographic record
Abstract
Abstract We used stable isotopes to determine spatial and temporal patterns of suspended kelp‐derived detritus (KDD) along a Macrocystis kelp abundance gradient driven by recovering sea otter (Enhydra lutris) populations along the west coast of Vancouver Island. Three dominant kelp species (order Laminariales) and surface marine size‐fractionated particulate organic matter (POM) were sampled along offshore transects (0–30 km) within three regions during summer and winter. Nearshore phytoplankton isotope values were identified in regions with high chlorophyll a concentrations. Summer blooming phytoplankton δ13C values were enriched by 2.4‰ and 0.8‰ and δ15N was enriched by 1.2‰ and 0.3‰ for 20–63 μm and 0.7–20 μm POM, respectively. Macrophyte stable isotope values varied significantly within and among regions, as well as between seasons. Primary producer values were used in a Bayesian isotope mixing model (MixSIR) to estimate the KDD contribution to POM. In general, the kelp abundant region had a greater contribution of KDD to POM further away (from 4 km to 30 km) from the kelp forest (range of medians 7–58%) compared to the kelp sparse region (4–30%). Seasonally, the KDD contribution to 20–63 μm POM size fractions was greater during summer (33% average) compared to winter (16% average). These results indicated that kelp abundance is not the only driver of KDD dispersal spatially and temporally. Factors such as local oceanography, large variation in kelp isotope values, and similarity between kelp and shelf phytoplankton isotope values affect these patterns and lead to high uncertainty in modeled KDD contributions.
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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 | 0.001 |
| 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".