Measurement · Productivity & Pigments · mgC/m³/hld

14C assimilation replicate 1

The registry carries no authored label for this measurement, so the name above is the canonical series' own description in metadata/measurement_type.csv (variable.csv holds the authored labels, five today).

key c14_rep1 series c14_rep1

15,542
values: 7
flagged: 2,289
sampling events: 1984–2020
years: 1
dataset: 0–211 m
depth: Jan · Feb · Mar · Apr · May · Jun · Jul · Aug · Sep · Oct · Nov
seasons

Heads-up 7 of these 15,542 values (under 0.1 %) are flagged by their provider as questionable or bad. The Explorer, the climatology and its anomalies leave them out; do the same when you read the data yourself (cc_qual_ok_sql() in calcofi4r, qual_ok_sql() in calcofi4py).

Measured in

Hydrographic Bottle 15,542 values · 2,289 sampling events · 1984–2020 · 0–211 m · 104 grid cells · 154 cruises

series c14_rep1 · source column c14as1 · flag column c14a1q replicate no bound declared no P01 concept 7 flagged

One row per series, one cell per year, darker with more values — the same strip the front door draws per dataset and a species page draws per taxon. Each row is one dataset's own measurement_type; the label is written once, in the title above.

By depth

Values per depth band at the release grain, one thin bar per series. The bands are the record's own (0-10 … 2000+ m).

By month

Values per calendar month, one row per series: CalCOFI's quarterly cruises show as peaks in the months the ships sail.

Range & quality

bounds: No physical bound is declared for this series in metadata/measurement_type.csv, so nothing is enforced at the ingest and the range below is simply what the release carries.
observed: Hydrographic Bottle c14_rep1 -0.24 · median 2.686 · 584.5 mgC/m³/hld (5th–95th percentile 0.06–43.3)
flags: Hydrographic Bottle c14_rep1 c14a1q on 7 of 15,542 rows: 8 7; qual_ok keeps 15,535. The codes are this dataset's own vocabulary, uninterpreted.
baseline: A monthly climatology exists for this measurement (the release's climatology table: 1993–2013, dataset × grid cell × calendar month × 10 m bin), so the Explorer can draw anomalies against it rather than values.

Ways in

Explorermaps, sections and time series of this measurement — opens prefilled
https://calcofi.io/explore/?var=c14_rep1
Explorer · a depth section vs normalthe newest cruise's section against the climatology baseline
https://calcofi.io/explore/?lens=section&var=c14_rep1&anom=1
db-querySQL in your browser — the shell opens with this query
https://calcofi.io/db-query/?sql=SELECT+%2A+FROM+__TBL%3Aobs_env__+WHERE+measurement_type+%3D+%27c14_rep1%27+LIMIT+100%3B
ERDDAP · Hydrographic BottleCalCOFI's own server, the Hydrographic Bottle table constrained to this series
https://erddap.calcofi.io/erddap/tabledap/calcofi_bottle.html?&measurement_type=%22c14_rep1%22
Parquetobs_env is partitioned by measurement_type in the content-addressed store — resolved through the release catalog, never a path built by hand
```
SELECT * FROM read_json('…/v2026.09.10/catalog.json')
-- objects[] WHERE table = 'obs_env'
--   AND partition_value = 'c14_rep1'
```

library(calcofi4r)
con <- cc_get_db()                      # the promoted release
tbl(con, "obs_env") |> filter(measurement_type %in% c("c14_rep1"))

Python

import calcofi4py as cc
con = cc.cc_get_db()
con.sql("SELECT * FROM obs_env WHERE measurement_type IN ('c14_rep1')").df()

This page as data

JSONthis measurement's entry of the release record, verbatim
https://calcofi.io/measurements/c14_rep1.json

Read from measurements.json of release v2026.09.10, written at release by calcofi4db::build_measurements_catalog() beside taxa.json. The catalog's index is /measurements/; the JSON-LD on this page is a schema.org/DefinedTerm in the NERC P01 term set with a PropertyValue per series.

Cite

Cite the release; each dataset's own citation, licence and DOI are on its page — Hydrographic Bottle. The Explorer's Share → Cite writes it for the view you make.